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# pylint: disable=unused-variable, invalid-name
"""1D convolution in python"""

import numpy as np

from tvm.topi.nn.utils import get_pad_tuple1d


def dilate_np(x, dilation):
    """1D dilation using numpy

    Parameters
    ----------
    x : numpy.ndarray
        Array to dilate with shape [batch, in_channel, in_width]

    dilation : int
        dilation rate of output

    Returns
    -------
    out : numpy.ndarray
        Dilated output with shape [batch, in_channel, (in_width - 1) * dilation + 1]
    """
    irange = range(len(x) - 1)
    for d in range(dilation - 1):
        indices = [(d + 1) * (i + 1) for i in irange]
        x = np.insert(x, indices, 0)
    return x


def group_conv1d_ncw_python(a_np, w_np, stride, padding, dilation, groups):
    "Grouped version of `conv1d_ncw_python`, see that for documentation"
    a_slices = np.array_split(a_np, groups, axis=1)
    w_slices = np.array_split(w_np, groups, axis=0)
    b_slices = [
        conv1d_ncw_python(a_slice, w_slice, stride, padding, dilation)
        for a_slice, w_slice in zip(a_slices, w_slices)
    ]
    return np.concatenate(b_slices, axis=1)


def conv1d_ncw_python(a_np, w_np, stride, padding, dilation):
    """1D convolution operator in NCW layout

    Parameters
    ----------
    a_np : numpy.ndarray
        3-D with shape [batch, in_channel, in_width]

    w_np : numpy.ndarray
        3-D with shape [num_filter, in_channel, filter_width]

    stride : int
        Stride size

    padding : int, tuple, or str
        Single int for padding size or tuple of (left, right) padding
        or a string in ['VALID', 'SAME']

    dilation : int
        Dilation rate of the kernel

    groups : int
        Number of groups in the convolution

    Returns
    -------
    b_np : numpy.ndarray
        3-D with shape [batch, out_channel, out_width]
    """
    batch, in_c, in_w = a_np.shape
    out_c, _, filter_w = w_np.shape
    if isinstance(stride, tuple | list):
        stride = stride[0]
    if isinstance(dilation, tuple | list):
        dilation = dilation[0]

    dilated_filter_w = (filter_w - 1) * dilation + 1
    pad_left, pad_right = get_pad_tuple1d(padding, (dilated_filter_w,))
    out_w = ((in_w - dilated_filter_w + pad_left + pad_right) // stride) + 1

    padded_a_np = np.zeros((batch, in_c, in_w + pad_left + pad_right))
    padded_a_np[:, :, pad_left : (in_w + pad_left)] = a_np

    b_np = np.zeros((batch, out_c, out_w))
    for n in range(batch):
        for f in range(out_c):
            for c in range(in_c):
                out = np.convolve(
                    padded_a_np[n, c], np.flip(dilate_np(w_np[f, c], dilation)), mode="valid"
                )
                b_np[n, f] += out[::stride]
    return b_np
