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# pylint: disable=invalid-name
"""TVM operator space_to_depth compute."""
from __future__ import absolute_import
import tvm
from tvm import te
from .. import tag


def space_to_depth(data, block_size, layout='NCHW'):
    """Perform space to depth transformation on the data

    Parameters
    ----------
    data : tvm.te.Tensor
        4-D tensor in either NCHW or NHWC layout.

    block_size : int
        Size of blocks to decompose into channel dimension.

    layout : string
        Either NCHW or NHWC, indicating data layout.

    Returns
    -------
    output : tvm.te.Tensor
        Output of shape [N, C * block_size**2, H / block_size, W / block_size]
    """

    if layout == 'NCHW':
        in_n, in_c, in_h, in_w = data.shape
        output_shape = [in_n, in_c * block_size * block_size,
                        tvm.tir.truncdiv(in_h, block_size), tvm.tir.truncdiv(in_w, block_size)]
    elif layout == 'NHWC':
        in_n, in_h, in_w, in_c = data.shape
        output_shape = [in_n, tvm.tir.truncdiv(in_h, block_size), tvm.tir.truncdiv(
            in_w, block_size), in_c * block_size * block_size]
    else:
        raise ValueError("Only NCHW and NHWC layouts are currently supported.")

    def _get_indices(*indices):
        if layout == 'NCHW':
            n, c, y, x = indices
        elif layout == 'NHWC':
            n, y, x, c = indices
        return n, c, y, x

    def _get_pixel(n, c, y, x):
        block_offset = tvm.tir.truncdiv(c, in_c)
        channel_idx = tvm.tir.truncmod(c, in_c)
        x_idx = tvm.tir.truncmod(block_offset, block_size)
        y_idx = tvm.tir.truncdiv(block_offset, block_size)

        if layout == 'NCHW':
            output = data(n, channel_idx, y_idx +
                          (y * block_size), x_idx + (x * block_size))
        else:
            output = data(n, y_idx + (y * block_size), x_idx +
                          (x * block_size), channel_idx)
        return output

    def _compute(*indices):
        n, c, y, x = _get_indices(*indices)
        return _get_pixel(n, c, y, x)

    return te.compute(output_shape, _compute, name='space_to_depth', tag=tag.INJECTIVE)
