import torch
import torch.nn as nn


class FCDiscriminator(nn.Module):

    def __init__(self, num_classes, ndf=64):
        super().__init__()
        self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
            padding=1)
        self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1
            )
        self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2,
            padding=1)
        self.conv4 = nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2,
            padding=1)
        self.classifier = nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2,
            padding=1)
        self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        x = self.conv1(x)
        x = self.leaky_relu(x)
        x = self.conv2(x)
        x = self.leaky_relu(x)
        x = self.conv3(x)
        x = self.leaky_relu(x)
        x = self.conv4(x)
        x = self.leaky_relu(x)
        x = self.classifier(x)
        return x


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


def get_init_inputs():
    return [[], {'num_classes': 4}]

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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@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 // 1024 % 64
    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_leaky_relu_1(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 // 256 % 128
    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_leaky_relu_2(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 // 64 % 256
    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_leaky_relu_3(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 // 16 % 512
    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_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    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
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


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, (64, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (64,), (1,))
    assert_size_stride(primals_3, (4, 4, 64, 64), (16384, 4096, 64, 1))
    assert_size_stride(primals_4, (128, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (256, 128, 4, 4), (2048, 16, 4, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (512, 256, 4, 4), (4096, 16, 4, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (1, 512, 4, 4), (8192, 16, 4, 1))
    assert_size_stride(primals_11, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 64, 32, 32), (65536, 1024, 32, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(262144)](buf1,
            primals_2, 262144, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 128, 16, 16), (32768, 256, 16, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_leaky_relu_1[grid(131072)](buf3,
            primals_5, 131072, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 256, 8, 8), (16384, 64, 8, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_leaky_relu_2[grid(65536)](buf5,
            primals_7, 65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 512, 4, 4), (8192, 16, 4, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_leaky_relu_3[grid(32768)](buf7,
            primals_9, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_9
        buf8 = extern_kernels.convolution(buf7, primals_10, stride=(2, 2),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 1, 2, 2), (4, 4, 2, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_4[grid(16)](buf9, primals_11, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_11
    return (buf9, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_10, buf1, buf3, buf5, buf7)


class FCDiscriminatorNew(nn.Module):

    def __init__(self, num_classes, ndf=64):
        super().__init__()
        self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2,
            padding=1)
        self.conv2 = nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1
            )
        self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2,
            padding=1)
        self.conv4 = nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2,
            padding=1)
        self.classifier = nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2,
            padding=1)
        self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = 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.classifier.weight
        primals_11 = self.classifier.bias
        primals_3 = 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]
