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
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


@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 1024
    y1 = yindex // 1024
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None)
    tl.store(out_ptr0 + (y0 + 1024 * x2 + 4194304 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 512
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 128
    y1 = yindex // 128
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 128 * x2 + 524288 * y1), ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp2, ymask)
    tl.store(out_ptr1 + (y0 + 128 * x2 + 524288 * y1), tmp2, ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 1024
    y1 = yindex // 1024
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 1024 * x2 + 4194304 * y1), None)
    tmp1 = tl.load(in_ptr1 + y0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp4, None)
    tl.store(out_ptr1 + (y0 + 1024 * x2 + 4194304 * y1), tmp6, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (128, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_2, (128,), (1,))
    assert_size_stride(primals_3, (4, 1024, 64, 64), (4194304, 4096, 64, 1))
    assert_size_stride(primals_4, (1024, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1024, 64, 64), (4194304, 1, 65536, 
            1024), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(4096, 4096)](primals_3, buf0, 4096, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 128, 64, 64), (524288, 1, 8192, 128))
        buf2 = empty_strided_cuda((4, 128, 64, 64), (524288, 4096, 64, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 128, 64, 64), (524288, 1, 8192, 128),
            torch.float32)
        triton_poi_fused_convolution_1[grid(512, 4096)](buf1, primals_2,
            buf2, buf3, 512, 4096, XBLOCK=32, YBLOCK=32, num_warps=4,
            num_stages=1)
        del buf1
        del primals_2
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 1024, 64, 64), (4194304, 1, 65536, 1024))
        del buf3
        buf5 = empty_strided_cuda((4, 1024, 64, 64), (4194304, 4096, 64, 1),
            torch.float32)
        buf6 = empty_strided_cuda((4, 1024, 64, 64), (4194304, 1, 65536, 
            1024), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(4096, 4096)
            ](buf4, primals_5, buf5, buf6, 4096, 4096, XBLOCK=32, YBLOCK=32,
            num_warps=4, num_stages=1)
        del buf4
        del primals_5
    return buf2, buf5, primals_1, buf0, primals_4, buf2, buf6


class AutoencoderNew(nn.Module):

    def __init__(self):
        super(AutoencoderNew, self).__init__()
        self.encoder = nn.Conv2d(1024, 128, kernel_size=1)
        self.decoder = nn.Conv2d(128, 1024, kernel_size=1)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_1 = self.encoder.weight
        primals_2 = self.encoder.bias
        primals_4 = self.decoder.weight
        primals_5 = self.decoder.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]


Measured latency_ms: 0.014737