# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/7z/c7zwaenh5um3ia5rjbsf6yda6kmvekfkarouguqg5q2uipjuvhoj.py
# Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   relu => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 143840
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3596) % 10
    x0 = xindex % 3596
    x4 = (xindex // 3596)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + (3616*x4)), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/nk/cnkd4znkwsgchhsdj3fjtvdgjug5wuch2mqqtgprce62bxpst5sh.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x => getitem, getitem_1
# Graph fragment:
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i8', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 46400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = (xindex // 20) % 58
    x2 = (xindex // 1160)
    x3 = xindex % 1160
    tmp0 = tl.load(in_ptr0 + ((3*x0) + (62*x1) + (3616*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (3*x0) + (62*x1) + (3616*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (3*x0) + (62*x1) + (3616*x2)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tmp1 > tmp0
    tmp6 = tl.full([1], 1, tl.int8)
    tmp7 = tl.full([1], 0, tl.int8)
    tmp8 = tl.where(tmp5, tmp6, tmp7)
    tmp9 = tmp3 > tmp2
    tmp10 = tl.full([1], 2, tl.int8)
    tmp11 = tl.where(tmp9, tmp10, tmp8)
    tl.store(out_ptr0 + (x3 + (1184*x2)), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + (1280*x2)), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7g/c7gmp6e24wfbdcnqvbuzbbu755fkwoq2swpsqrebpsh4rp3tjwzt.py
# Topologically Sorted Source Nodes: [conv2d_1, relu_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   relu_1 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 80640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 1008) % 20
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/g6/cg6r6dike23cyqqwfqegj6rcuzzn7pmc2rblhy7luc6uem6fyrc7.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_1 => _low_memory_max_pool2d_with_offsets_1, getitem_3
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_1, [1, 3], [1, 3], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_3 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets_1, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 26880
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 6720
    x1 = (xindex // 6720)
    tmp0 = tl.load(in_ptr0 + (3*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (3*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (3*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(out_ptr0 + (x0 + (6784*x1)), tmp10, xmask)
    tl.store(out_ptr1 + (x2), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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, (10, 3, 7, 3), (63, 21, 3, 1))
    assert_size_stride(primals_2, (10, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (20, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_5, (20, ), (1, ))
    assert_size_stride(primals_6, (100, 1120), (1120, 1))
    assert_size_stride(primals_7, (100, ), (1, ))
    assert_size_stride(primals_8, (1, 100), (100, 1))
    assert_size_stride(primals_9, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 10, 58, 62), (35960, 3596, 62, 1))
        buf1 = empty_strided_cuda((4, 10, 58, 62), (36160, 3616, 62, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, relu], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf0, primals_2, buf1, 143840, grid=grid(143840), stream=stream0)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 58, 20), (11840, 1184, 20, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 10, 58, 20), (12800, 1280, 20, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 46400, grid=grid(46400), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 56, 18), (20160, 1008, 18, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, relu_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf5, primals_5, 80640, grid=grid(80640), stream=stream0)
        del primals_5
        buf6 = empty_strided_cuda((4, 20, 56, 6), (6784, 336, 6, 1), torch.int8)
        buf7 = empty_strided_cuda((4, 20, 56, 6), (6720, 336, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf5, buf6, buf7, 26880, grid=grid(26880), stream=stream0)
        buf8 = empty_strided_cuda((24, 100), (100, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf7, (24, 1120), (1, 24), 0), reinterpret_tensor(primals_6, (1120, 100), (1, 1120), 0), alpha=1, beta=1, out=buf8)
        del primals_7
        buf10 = empty_strided_cuda((24, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8, (100, 1), (1, 100), 0), alpha=1, beta=1, out=buf10)
        del primals_9
    return (reinterpret_tensor(buf10, (24, ), (1, ), 0), primals_1, primals_3, primals_4, buf1, buf2, buf3, buf5, buf6, reinterpret_tensor(buf7, (24, 1120), (1, 24), 0), reinterpret_tensor(primals_6, (1120, 100), (1, 1120), 0), buf8, primals_8, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((10, 3, 7, 3), (63, 21, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((10, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((20, 10, 3, 3), (90, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((100, 1120), (1120, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((100, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, 100), (100, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from torch import nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=
            (7, 3))
        self.pool = nn.MaxPool2d(kernel_size=(1, 3))
        self.conv2 = nn.Conv2d(in_channels=10, out_channels=20, kernel_size
            =(3, 3))
        self.fc1 = nn.Linear(7 * 8 * 20, 100)
        self.fc2 = nn.Linear(100, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(7 * 8 * 20, -1)
        x = x.permute(1, 0)
        x = self.fc1(x)
        x = self.fc2(x)
        x = torch.squeeze(x)
        return x


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {}]

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 import 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_convolution_relu_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 143840
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3596 % 10
    x0 = xindex % 3596
    x4 = xindex // 3596
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 3616 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 46400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 20
    x1 = xindex // 20 % 58
    x2 = xindex // 1160
    x3 = xindex % 1160
    tmp0 = tl.load(in_ptr0 + (3 * x0 + 62 * x1 + 3616 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 3 * x0 + 62 * x1 + 3616 * x2), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 3 * x0 + 62 * x1 + 3616 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tmp1 > tmp0
    tmp6 = tl.full([1], 1, tl.int8)
    tmp7 = tl.full([1], 0, tl.int8)
    tmp8 = tl.where(tmp5, tmp6, tmp7)
    tmp9 = tmp3 > tmp2
    tmp10 = tl.full([1], 2, tl.int8)
    tmp11 = tl.where(tmp9, tmp10, tmp8)
    tl.store(out_ptr0 + (x3 + 1184 * x2), tmp4, xmask)
    tl.store(out_ptr1 + (x3 + 1280 * x2), tmp11, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 80640
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 1008 % 20
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 26880
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 6720
    x1 = xindex // 6720
    tmp0 = tl.load(in_ptr0 + 3 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 3 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 3 * x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tl.store(out_ptr0 + (x0 + 6784 * x1), tmp10, xmask)
    tl.store(out_ptr1 + x2, tmp11, 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, (10, 3, 7, 3), (63, 21, 3, 1))
    assert_size_stride(primals_2, (10,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (20, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (100, 1120), (1120, 1))
    assert_size_stride(primals_7, (100,), (1,))
    assert_size_stride(primals_8, (1, 100), (100, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 10, 58, 62), (35960, 3596, 62, 1))
        buf1 = empty_strided_cuda((4, 10, 58, 62), (36160, 3616, 62, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(143840)](buf0, primals_2,
            buf1, 143840, XBLOCK=512, num_warps=8, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 10, 58, 20), (11840, 1184, 20, 1),
            torch.float32)
        buf3 = empty_strided_cuda((4, 10, 58, 20), (12800, 1280, 20, 1),
            torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(46400)](buf1, buf2,
            buf3, 46400, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 20, 56, 18), (20160, 1008, 18, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(80640)](buf5, primals_5, 
            80640, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 20, 56, 6), (6784, 336, 6, 1), torch.int8
            )
        buf7 = empty_strided_cuda((4, 20, 56, 6), (6720, 336, 6, 1), torch.
            float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(26880)](buf5, buf6,
            buf7, 26880, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((24, 100), (100, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf7, (24, 1120),
            (1, 24), 0), reinterpret_tensor(primals_6, (1120, 100), (1, 
            1120), 0), alpha=1, beta=1, out=buf8)
        del primals_7
        buf10 = empty_strided_cuda((24, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf8, reinterpret_tensor(primals_8,
            (100, 1), (1, 100), 0), alpha=1, beta=1, out=buf10)
        del primals_9
    return (reinterpret_tensor(buf10, (24,), (1,), 0), primals_1, primals_3,
        primals_4, buf1, buf2, buf3, buf5, buf6, reinterpret_tensor(buf7, (
        24, 1120), (1, 24), 0), reinterpret_tensor(primals_6, (1120, 100),
        (1, 1120), 0), buf8, primals_8)


class NetNew(nn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=
            (7, 3))
        self.pool = nn.MaxPool2d(kernel_size=(1, 3))
        self.conv2 = nn.Conv2d(in_channels=10, out_channels=20, kernel_size
            =(3, 3))
        self.fc1 = nn.Linear(7 * 8 * 20, 100)
        self.fc2 = nn.Linear(100, 1)
        self.sigmoid = nn.Sigmoid()

    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.fc1.weight
        primals_7 = self.fc1.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.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])
        return output[0]
