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 import 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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_mul_1(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 208
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 13 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 * tmp4
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(out_ptr0 + x3, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (), ())
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](primals_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (4, 4, 
            16), (64, 16, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (4, 4, 13), (52, 13, 1))
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 13), (52, 13, 1), torch.float32)
        triton_poi_fused_convolution_mul_1[grid(208)](buf2, primals_3,
            primals_4, buf3, 208, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
    return buf3, primals_2, primals_4, reinterpret_tensor(buf0, (4, 4, 16),
        (64, 16, 1), 0), buf2


class IntervalObservationEncoderNew(nn.Module):

    def __init__(self, num_input_channel: 'int', num_output_channel: 'int',
        kernel_size: 'int', initial_output_weight_value: 'float'):
        super().__init__()
        assert initial_output_weight_value <= 1
        self.conv_1d = nn.Conv1d(in_channels=num_input_channel,
            out_channels=num_output_channel, kernel_size=kernel_size, stride=1)
        self.weight = nn.Parameter(torch.tensor(initial_output_weight_value
            ).type(torch.FloatTensor), requires_grad=True)

    def forward(self, input_0):
        primals_4 = self.weight
        primals_2 = self.conv_1d.weight
        primals_3 = self.conv_1d.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.014983