import numpy as np
import hypothesis.strategies as st
import unittest
import caffe2.python.hypothesis_test_util as hu
from caffe2.python import core, workspace
from hypothesis import given, settings
import caffe2.python.ideep_test_util as mu

@st.composite
def _tensor_splits(draw, add_axis=False):
    """Generates (axis, split_info, tensor_splits) tuples."""
    tensor = draw(hu.tensor(min_dim=2, min_value=4))  # Each dim has at least 4 elements.
    axis = draw(st.integers(-len(tensor.shape), len(tensor.shape) - 1))
    if add_axis:
        # Simple case: get individual slices along one axis, where each of them
        # is (N-1)-dimensional. The axis will be added back upon concatenation.
        return (
            axis,
            np.ones(tensor.shape[axis], dtype=np.int32),
            [
                np.array(tensor.take(i, axis=axis))
                for i in range(tensor.shape[axis])
            ]
        )
    else:
        # General case: pick some (possibly consecutive, even non-unique)
        # indices at which we will split the tensor, along the given axis.
        splits = sorted(draw(
            st.lists(elements=st.integers(0, tensor.shape[axis]), max_size=4)
        ) + [0, tensor.shape[axis]])
        # Not support empty tensor
        splits = list(set(splits))
        return (
            axis,
            np.array(np.diff(splits), dtype=np.int32),
            [
                tensor.take(range(splits[i], splits[i + 1]), axis=axis)
                for i in range(len(splits) - 1)
            ],
        )


@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class TestConcatSplitOps(hu.HypothesisTestCase):
    @given(tensor_splits=_tensor_splits(),
           **mu.gcs)
    @settings(deadline=10000)
    def test_concat(self, tensor_splits, gc, dc):
        axis, _, splits = tensor_splits

        op = core.CreateOperator(
            "Concat",
            ['X_{}'.format(i) for i in range(len(splits))],
            ['concat_result', 'split_info'],
            axis=axis
        )

        self.assertDeviceChecks(dc, op, splits, [0, 1])
        self.assertGradientChecks(gc, op, splits, 0, [0])

    @given(tensor_splits=_tensor_splits(),
           split_as_arg=st.booleans(),
           **mu.gcs)
    @settings(deadline=10000)
    def test_split(self, tensor_splits, split_as_arg, gc, dc):
        axis, split_info, splits = tensor_splits

        split_as_arg = True

        if split_as_arg:
            input_names = ['input']
            input_tensors = [np.concatenate(splits, axis=axis)]
            kwargs = dict(axis=axis, split=split_info)
        else:
            input_names = ['input', 'split']
            input_tensors = [np.concatenate(splits, axis=axis), split_info]
            kwargs = dict(axis=axis)

        op = core.CreateOperator(
            "Split",
            input_names,
            ['X_{}'.format(i) for i in range(len(split_info))],
            **kwargs
        )

        def split_ref(input, split=split_info):
            s = np.cumsum([0] + list(split))
            return [
                np.array(input.take(np.arange(s[i], s[i + 1]), axis=axis))
                for i in range(len(split))
            ]
        outputs_with_grad = range(len(split_info))
        self.assertDeviceChecks(dc, op, input_tensors, outputs_with_grad)
        self.assertGradientChecks(gc, op, input_tensors, 0, outputs_with_grad)

    @given(tensor_splits=_tensor_splits(add_axis=True), **mu.gcs)
    @settings(deadline=10000)
    def test_concat_add_axis(self, tensor_splits, gc, dc):
        axis, _, splits = tensor_splits
        op = core.CreateOperator(
            "Concat",
            ['X_{}'.format(i) for i in range(len(splits))],
            ['concat_result', 'split_info'],
            axis=axis,
            add_axis=1
        )

        self.assertDeviceChecks(dc, op, splits, [0, 1])

        for i in range(len(splits)):
            self.assertGradientChecks(gc, op, splits, i, [0])


    @given(tensor_splits=_tensor_splits(add_axis=True), **mu.gcs)
    def test_concat_with_TensorCPU(self, tensor_splits, gc, dc):
        axis, _, splits = tensor_splits
        op0 = core.CreateOperator(
            "Concat",
            ['X_{}'.format(i) for i in range(len(splits))],
            ['concat_result0', 'split_info0'],
            axis=axis,
            add_axis=1,
            device_option=dc[0]
        )
        op1 = core.CreateOperator(
            "Concat",
            ['X_{}'.format(i) for i in range(len(splits))],
            ['concat_result1', 'split_info1'],
            axis=axis,
            add_axis=1,
            device_option=dc[1]
        )

        for i, X in enumerate(splits):
            workspace.FeedBlob('X_{}'.format(i), X, dc[0])

        workspace.RunOperatorOnce(op0)
        res0 = workspace.FetchBlob('concat_result0')
        inf0 = workspace.FetchBlob('split_info0')

        workspace.RunOperatorOnce(op1)
        res1 = workspace.FetchBlob('concat_result1')
        inf1 = workspace.FetchBlob('split_info1')

        if not np.allclose(res0, res1, atol=0.0, rtol=0.0):
            print(res1.flatten())
            print(res0.flatten())
            print(np.max(np.abs(res1 - res0)))
            self.assertTrue(False)

        if not np.allclose(inf0, inf1, atol=0.0, rtol=0.0):
            print(inf1.flatten())
            print(inf0.flatten())
            print(np.max(np.abs(inf1 - inf0)))
            self.assertTrue(False)


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