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_cat_0(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
    x1 = xindex // 16 % 2
    x0 = xindex % 16
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x2), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 16 * x2), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 16 % 32
    tmp0 = tl.load(in_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.2
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, None)
    tl.store(out_ptr1 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_3, (32, 2, 5, 5), (50, 25, 5, 1))
    assert_size_stride(primals_4, (32,), (1,))
    assert_size_stride(primals_5, (32, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_6, (32,), (1,))
    assert_size_stride(primals_7, (32, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_8, (32,), (1,))
    assert_size_stride(primals_9, (1, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_10, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, buf0, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = extern_kernels.convolution(buf0, primals_3, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 32, 4, 4), (512, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.float32
            )
        triton_poi_fused_convolution_leaky_relu_1[grid(2048)](buf1,
            primals_4, buf2, buf3, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_4
        buf4 = extern_kernels.convolution(buf3, primals_5, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 4, 4), (512, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        buf6 = buf1
        del buf1
        triton_poi_fused_convolution_leaky_relu_1[grid(2048)](buf4,
            primals_6, buf5, buf6, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_6
        buf7 = extern_kernels.convolution(buf6, primals_7, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 32, 4, 4), (512, 16, 4, 1))
        buf8 = empty_strided_cuda((4, 32, 4, 4), (512, 16, 4, 1), torch.bool)
        buf9 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_1[grid(2048)](buf7,
            primals_8, buf8, buf9, 2048, XBLOCK=256, num_warps=4, num_stages=1)
        del buf7
        del primals_8
        buf10 = extern_kernels.convolution(buf9, primals_9, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 1, 4, 4), (16, 16, 4, 1))
        buf11 = buf10
        del buf10
        buf12 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(64)](buf11,
            primals_10, buf12, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_10
    return (buf11, primals_3, primals_5, primals_7, primals_9, buf0, buf2,
        buf3, buf5, buf6, buf8, buf9, buf12)


def get_conv2d_layer(in_c, out_c, k, s, p=0, dilation=1, groups=1):
    return nn.Conv2d(in_channels=in_c, out_channels=out_c, kernel_size=k,
        stride=s, padding=p, dilation=dilation, groups=groups)


class Adjust_naiveNew(nn.Module):

    def __init__(self, opt):
        super().__init__()
        self.conv1 = get_conv2d_layer(in_c=2, out_c=32, k=5, s=1, p=2)
        self.conv2 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
        self.conv3 = get_conv2d_layer(in_c=32, out_c=32, k=5, s=1, p=2)
        self.conv4 = get_conv2d_layer(in_c=32, out_c=1, k=5, s=1, p=2)
        self.leaky_relu = nn.LeakyReLU(0.2)
        self.relu = nn.ReLU()

    def forward(self, input_0, input_1):
        primals_3 = self.conv1.weight
        primals_4 = self.conv1.bias
        primals_5 = self.conv2.weight
        primals_6 = self.conv2.bias
        primals_7 = self.conv3.weight
        primals_8 = self.conv3.bias
        primals_9 = self.conv4.weight
        primals_10 = self.conv4.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.016978