import torch
import torch.nn.functional as F
import torch.nn as nn


class ConvNorm(nn.Module):
    """ 1D Convolution """

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super(ConvNorm, self).__init__()
        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            bias=bias)

    def forward(self, signal):
        conv_signal = self.conv(signal)
        return conv_signal


class Conv1DBlock(nn.Module):
    """ 1D Convolutional Block """

    def __init__(self, in_channels, out_channels, kernel_size, activation=
        None, dropout=None):
        super(Conv1DBlock, self).__init__()
        self.conv_layer = nn.Sequential()
        self.conv_layer.add_module('conv_layer', ConvNorm(in_channels,
            out_channels, kernel_size=kernel_size, stride=1, padding=int((
            kernel_size - 1) / 2), dilation=1, w_init_gain='tanh'))
        if activation is not None:
            self.conv_layer.add_module('activ', activation)
        self.dropout = dropout

    def forward(self, x, mask=None):
        x = x.contiguous().transpose(1, 2)
        x = self.conv_layer(x)
        if self.dropout is not None:
            x = F.dropout(x, self.dropout, self.training)
        x = x.contiguous().transpose(1, 2)
        if mask is not None:
            x = x.masked_fill(mask.unsqueeze(-1), 0)
        return x


def get_inputs():
    return [torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 3), (12, 3, 1))
        del buf0
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(48)](buf2, primals_3, 48,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
    return reinterpret_tensor(buf2, (4, 3, 4), (12, 1, 3), 0
        ), primals_2, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0)


class ConvNorm(nn.Module):
    """ 1D Convolution """

    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
        padding=None, dilation=1, bias=True, w_init_gain='linear'):
        super(ConvNorm, self).__init__()
        if padding is None:
            assert kernel_size % 2 == 1
            padding = int(dilation * (kernel_size - 1) / 2)
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size=
            kernel_size, stride=stride, padding=padding, dilation=dilation,
            bias=bias)

    def forward(self, signal):
        conv_signal = self.conv(signal)
        return conv_signal


class Conv1DBlockNew(nn.Module):
    """ 1D Convolutional Block """

    def __init__(self, in_channels, out_channels, kernel_size, activation=
        None, dropout=None):
        super(Conv1DBlockNew, self).__init__()
        self.conv_layer = nn.Sequential()
        self.conv_layer.add_module('conv_layer', ConvNorm(in_channels,
            out_channels, kernel_size=kernel_size, stride=1, padding=int((
            kernel_size - 1) / 2), dilation=1, w_init_gain='tanh'))
        if activation is not None:
            self.conv_layer.add_module('activ', activation)
        self.dropout = dropout

    def forward(self, input_0):
        primals_1 = self.conv_layer.conv_layer.conv.weight
        primals_3 = self.conv_layer.conv_layer.conv.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
