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
from torch._inductor.runtime import triton_helpers
from torch import nn
import torch.utils.data
import torch.onnx.operators
import torch.optim
import torch.optim.lr_scheduler
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, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 112
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 7
    x2 = xindex // 28
    x3 = xindex % 28
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = tl.load(in_ptr0 + (x3 + 16 * x2), tmp2 & xmask, other=0.0)
    tl.store(out_ptr0 + x4, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 7
    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 + 28 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 7 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_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
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, 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, 7, 4), (28, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(112)](primals_1, buf0, 112,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 7), (28, 7, 1), torch.float32)
        triton_poi_fused_convolution_1[grid(16, 7)](buf0, buf1, 16, 7,
            XBLOCK=8, YBLOCK=16, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
        del buf1
        buf3 = reinterpret_tensor(buf2, (4, 4, 4), (16, 1, 4), 0)
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(64)](buf3,
            primals_3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_3
    return buf3, primals_2, reinterpret_tensor(buf0, (4, 4, 7), (28, 1, 4), 0
        ), buf4


def TemporalConvLayer(input_channels, output_channels, kernel_size):
    m = nn.Conv1d(in_channels=input_channels, out_channels=output_channels,
        kernel_size=kernel_size)
    nn.init.xavier_normal_(m.weight)
    return m


class TemporalBlockNew(nn.Module):

    def __init__(self, input_size, output_size, num_channels, kernel_size,
        dropout):
        super().__init__()
        self.pad = nn.ZeroPad2d((0, 0, 0, kernel_size - 1))
        self.tconv = TemporalConvLayer(input_size, num_channels, kernel_size)
        self.relu = nn.ReLU()

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


Measured latency_ms: 0.030606