from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn


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 Illumination_Alone(nn.Module):

    def __init__(self, opts):
        super().__init__()
        self.opts = opts
        self.conv1 = get_conv2d_layer(in_c=1, 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=32, k=5, s=1, p=2)
        self.conv5 = get_conv2d_layer(in_c=32, out_c=1, k=1, s=1, p=0)
        self.leaky_relu_1 = nn.LeakyReLU(0.2, inplace=True)
        self.leaky_relu_2 = nn.LeakyReLU(0.2, inplace=True)
        self.leaky_relu_3 = nn.LeakyReLU(0.2, inplace=True)
        self.leaky_relu_4 = nn.LeakyReLU(0.2, inplace=True)
        self.relu = nn.ReLU()

    def forward(self, l):
        x = l
        x1 = self.leaky_relu_1(self.conv1(x))
        x2 = self.leaky_relu_2(self.conv2(x1))
        x3 = self.leaky_relu_3(self.conv3(x2))
        x4 = self.leaky_relu_4(self.conv4(x3))
        x5 = self.relu(self.conv5(x4))
        return x5


def get_inputs():
    return [torch.rand([4, 1, 64, 64])]


def get_init_inputs():
    return [[], {'opts': _mock_config()}]

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_convolution_leaky_relu_0(in_out_ptr0, in_ptr0, 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 // 4096 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + 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(in_out_ptr0 + x3, tmp7, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_1(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    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, None)
    tl.store(out_ptr0 + x0, tmp7, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_2, (32, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_3, (32,), (1,))
    assert_size_stride(primals_4, (32, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (32, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_7, (32,), (1,))
    assert_size_stride(primals_8, (32, 32, 5, 5), (800, 25, 5, 1))
    assert_size_stride(primals_9, (32,), (1,))
    assert_size_stride(primals_10, (1, 32, 1, 1), (32, 1, 1, 1))
    assert_size_stride(primals_11, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1, 
            1), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf1,
            primals_3, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf3,
            primals_5, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, 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, 64, 64), (131072, 4096, 64, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf5,
            primals_7, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 32, 64, 64), (131072, 4096, 64, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_leaky_relu_0[grid(524288)](buf7,
            primals_9, 524288, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf9 = buf8
        del buf8
        buf10 = empty_strided_cuda((4, 1, 64, 64), (4096, 4096, 64, 1),
            torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_1[grid(16384)](
            buf9, primals_11, buf10, 16384, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_11
    return (buf9, primals_1, primals_2, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7, buf10)


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 Illumination_AloneNew(nn.Module):

    def __init__(self, opts):
        super().__init__()
        self.opts = opts
        self.conv1 = get_conv2d_layer(in_c=1, 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=32, k=5, s=1, p=2)
        self.conv5 = get_conv2d_layer(in_c=32, out_c=1, k=1, s=1, p=0)
        self.leaky_relu_1 = nn.LeakyReLU(0.2, inplace=True)
        self.leaky_relu_2 = nn.LeakyReLU(0.2, inplace=True)
        self.leaky_relu_3 = nn.LeakyReLU(0.2, inplace=True)
        self.leaky_relu_4 = nn.LeakyReLU(0.2, inplace=True)
        self.relu = nn.ReLU()

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.conv5.weight
        primals_11 = self.conv5.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
