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
from torch._inductor.runtime.triton_helpers import libdevice
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 25
    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]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 25 * y3), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 64 * x2 + 1600 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_tanh_1(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    xnumel = 784
    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]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 784 * y3), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(out_ptr0 + (y0 + 64 * x2 + 50176 * y1), tmp3, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 50176
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64 % 14
    x2 = xindex // 896
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 128 * x1 + 3584 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 128 * x1 + 3584 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (1792 + x0 + 128 * x1 + 3584 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (1856 + x0 + 128 * x1 + 3584 * x2), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_convolution_tanh_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)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_4(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 100
    xnumel = 128
    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]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 5
    y1 = yindex // 5
    y5 = yindex
    y4 = yindex // 25
    y6 = yindex % 25
    tmp0 = tl.load(in_ptr0 + (x2 + 256 * y0 + 2560 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (128 + x2 + 256 * y0 + 2560 * y1), xmask &
        ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (1280 + x2 + 256 * y0 + 2560 * y1), xmask &
        ymask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (1408 + x2 + 256 * y0 + 2560 * y1), xmask &
        ymask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1, 1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1, 1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x2 + 128 * y5), tmp15, xmask & ymask)
    tl.store(out_ptr1 + (y6 + 25 * x2 + 3200 * y4), tmp16, xmask & ymask)


@triton.jit
def triton_poi_fused_tanh_5(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)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


@triton.jit
def triton_poi_fused_tanh_6(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)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


@triton.jit
def triton_poi_fused_sigmoid_7(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, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 28, 28), (784, 784, 28, 1))
    assert_size_stride(primals_2, (64, 1, 5, 5), (25, 25, 5, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (128, 64, 5, 5), (1600, 25, 5, 1))
    assert_size_stride(primals_5, (128,), (1,))
    assert_size_stride(primals_6, (1024, 3200), (3200, 1))
    assert_size_stride(primals_7, (1024,), (1,))
    assert_size_stride(primals_8, (512, 1024), (1024, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (1, 512), (512, 1))
    assert_size_stride(primals_11, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((128, 64, 5, 5), (1600, 1, 320, 64),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(8192, 25)](primals_4, buf0, 8192, 25,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf1 = 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(buf1, (4, 64, 28, 28), (50176, 784, 28, 1))
        buf2 = empty_strided_cuda((4, 64, 28, 28), (50176, 1, 1792, 64),
            torch.float32)
        triton_poi_fused_convolution_tanh_1[grid(256, 784)](buf1, primals_3,
            buf2, 256, 784, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf1
        del primals_3
        buf3 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
            torch.float32)
        buf4 = empty_strided_cuda((4, 64, 14, 14), (12544, 1, 896, 64),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_2[grid(50176)](buf2, buf3,
            buf4, 50176, XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = extern_kernels.convolution(buf3, buf0, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 128, 10, 10), (12800, 1, 1280, 128))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_tanh_3[grid(51200)](buf6, primals_5, 
            51200, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf7 = empty_strided_cuda((4, 128, 5, 5), (3200, 1, 640, 128),
            torch.int8)
        buf8 = empty_strided_cuda((4, 128, 5, 5), (3200, 25, 5, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_4[grid(100, 128)](buf6,
            buf7, buf8, 100, 128, XBLOCK=128, YBLOCK=2, num_warps=4,
            num_stages=1)
        buf9 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (4, 3200), (3200, 1), 0),
            reinterpret_tensor(primals_6, (3200, 1024), (1, 3200), 0), out=buf9
            )
        buf10 = buf9
        del buf9
        triton_poi_fused_tanh_5[grid(4096)](buf10, primals_7, 4096, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_7
        buf11 = empty_strided_cuda((4, 512), (512, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_8, (1024, 512),
            (1, 1024), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_tanh_6[grid(2048)](buf12, primals_9, 2048, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf13 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_10, (512, 1), (
            1, 512), 0), out=buf13)
        buf14 = buf13
        del buf13
        triton_poi_fused_sigmoid_7[grid(4)](buf14, primals_11, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_11
    return (buf14, primals_2, buf0, primals_1, buf2, buf3, buf4, buf6, buf7,
        reinterpret_tensor(buf8, (4, 3200), (3200, 1), 0), buf10, buf12,
        buf14, primals_10, primals_8, primals_6)


class DiscriminatorNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=5, padding=2)
        self.activation1 = nn.Tanh()
        self.maxpool1 = nn.MaxPool2d(kernel_size=(2, 2))
        self.conv2 = nn.Conv2d(64, 128, kernel_size=(5, 5))
        self.activation2 = nn.Tanh()
        self.maxpool2 = nn.MaxPool2d(kernel_size=(2, 2))
        self.fc1 = nn.Linear(128 * 5 * 5, 1024)
        self.activation3 = nn.Tanh()
        self.fc2 = nn.Linear(1024, 512)
        self.activation4 = nn.Tanh()
        self.fc3 = nn.Linear(512, 1)
        self.activation5 = nn.Sigmoid()

    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.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_10 = self.fc3.weight
        primals_11 = self.fc3.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]


Measured latency_ms: 0.164222