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
import torch.nn.functional as F
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_constant_pad_nd_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 5
    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
    x1 = xindex
    y0 = yindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-16 + y0 + 16 * x1), tmp2 & xmask & ymask,
        eviction_policy='evict_last', other=0.0)
    tl.store(out_ptr0 + (x1 + 5 * y0), tmp3, xmask & ymask)


@triton.jit
def triton_poi_fused_constant_pad_nd_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 96
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 6
    x3 = xindex // 6
    x1 = xindex // 6 % 4
    x4 = xindex
    tmp0 = -2 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-2 + x0 + 4 * x3), tmp2 & xmask, other=0.0)
    tmp4 = tl.load(in_ptr1 + x1, tmp2 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp5 = tmp3 + tmp4
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp2, tmp5, tmp6)
    tl.store(out_ptr0 + x4, tmp7, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x3 = xindex // 8
    x1 = xindex // 8 % 4
    x4 = xindex
    tmp0 = -4 + x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.load(in_ptr0 + (-4 + x0 + 4 * x3), tmp2 & xmask, other=0.0)
    tmp4 = tl.load(in_ptr1 + x1, tmp2 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp5 = tmp3 + tmp4
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp2, tmp5, tmp6)
    tl.store(out_ptr0 + x4, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 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, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 5), (20, 5, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(16, 5)](primals_1, buf0, 16,
            5, XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4), (16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 6), (24, 6, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_1[grid(96)](buf1, primals_3, buf2,
            96, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
            padding=(0,), dilation=(2,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 4), (16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(128)](buf3, primals_5, buf4,
            128, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1,),
            padding=(0,), dilation=(4,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4), (16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_3[grid(64)](buf6, primals_7, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_7
    return reinterpret_tensor(buf6, (4, 4, 4), (1, 16, 4), 0
        ), primals_2, primals_4, primals_6, buf0, buf2, buf4


class CausalConv1d(nn.Conv1d):

    def __init__(self, input_size, hidden_size, kernel_size, stride=1,
        dilation=1, groups=1, bias=True, sigmoid=None, tanh=None):
        self.left_padding = (kernel_size - 1) * dilation
        super(CausalConv1d, self).__init__(input_size, hidden_size,
            kernel_size, stride=stride, padding=0, dilation=dilation,
            groups=groups, bias=bias)

    def forward(self, input):
        x = F.pad(input.permute(1, 2, 0), (self.left_padding, 0))
        conv_out = super(CausalConv1d, self).forward(x)
        return conv_out.permute(2, 0, 1)


class WaveNew(nn.Module):

    def __init__(self, input_size, hidden_size, layers=3, activation='tanh'):
        super(WaveNew, self).__init__()
        self.layers = []
        prev_size = input_size
        for layer in range(layers):
            conv = CausalConv1d(prev_size, hidden_size, kernel_size=2,
                dilation=2 ** layer)
            self.layers.append(conv)
            self.add_module('layer' + str(layer), conv)
            prev_size = hidden_size

    def forward(self, input_0):
        primals_2 = self.layer0.weight
        primals_3 = self.layer0.bias
        primals_4 = self.layer1.weight
        primals_5 = self.layer1.bias
        primals_6 = self.layer2.weight
        primals_7 = self.layer2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.084986