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 numpy as np
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_leaky_relu_0(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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x0, 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 + x2, tmp4, None)
    tl.store(out_ptr1 + x2, tmp7, None)


@triton.jit
def triton_poi_fused_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, 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 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_sigmoid_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 4
    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.sigmoid(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (1024, 4), (4, 1))
    assert_size_stride(primals_3, (1024,), (1,))
    assert_size_stride(primals_4, (512, 1024), (1024, 1))
    assert_size_stride(primals_5, (512,), (1,))
    assert_size_stride(primals_6, (256, 512), (512, 1))
    assert_size_stride(primals_7, (256,), (1,))
    assert_size_stride(primals_8, (1, 256), (256, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 1024
            ), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 1024), (1024, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(4096)](buf0, primals_3, buf1,
            buf2, 4096, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_3
        buf3 = torch.ops.aten.native_dropout.default(buf2, 0.3, True)
        del buf2
        buf4 = buf3[0]
        buf5 = buf3[1]
        del buf3
        buf6 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(buf4, reinterpret_tensor(primals_4, (1024, 512),
            (1, 1024), 0), out=buf6)
        buf7 = empty_strided_cuda((4, 512), (512, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(2048)](buf6, primals_5, buf7,
            buf8, 2048, XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
        del primals_5
        buf9 = torch.ops.aten.native_dropout.default(buf8, 0.3, True)
        del buf8
        buf10 = buf9[0]
        buf11 = buf9[1]
        del buf9
        buf12 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_6, (512, 256),
            (1, 512), 0), out=buf12)
        buf13 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        buf14 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        triton_poi_fused_leaky_relu_2[grid(1024)](buf12, primals_7, buf13,
            buf14, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del buf12
        del primals_7
        buf15 = torch.ops.aten.native_dropout.default(buf14, 0.3, True)
        del buf14
        buf16 = buf15[0]
        buf17 = buf15[1]
        del buf15
        buf18 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf16, reinterpret_tensor(primals_8, (256, 1), (1,
            256), 0), out=buf18)
        buf19 = buf18
        del buf18
        triton_poi_fused_sigmoid_3[grid(4)](buf19, primals_9, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_9
    return (buf19, primals_1, buf1, buf4, buf5, buf7, buf10, buf11, buf13,
        buf16, buf17, buf19, primals_8, primals_6, primals_4)


class DiscriminatorNew(nn.Module):

    def __init__(self, img_shape, hidden_dim=1024):
        super().__init__()
        in_dim = int(np.prod(img_shape))
        self.fc1 = nn.Linear(in_dim, hidden_dim)
        self.fc2 = nn.Linear(self.fc1.out_features, self.fc1.out_features // 2)
        self.fc3 = nn.Linear(self.fc2.out_features, self.fc2.out_features // 2)
        self.fc4 = nn.Linear(self.fc3.out_features, 1)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.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])
        return output[0]


Measured latency_ms: 0.034001