# 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_7/inductor_cache/i7/ci7iuesg5il6wqqzmaswgctkzbwymmbj4mwpwk52cm2x3r2pgw2f.py
# Topologically Sorted Source Nodes: [conv1d, relu], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv1d => convolution
#   relu => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view, %primals_2, %primals_3, [1], [2], [1], False, [0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %le_1 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_threshold_backward_0', '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_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 2400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 100) % 6
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x3), tmp4, xmask)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/au/cauq77vvgevknppiy5hruyfcgu7jogj2d674yn6n2r2j5yeuueyt.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, getitem_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze, [1, 10], [1, 10], [0, 0], [1, 1], False), 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=[256], 
    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': 10, '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 = 240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (10*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (4 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (5 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (6 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + (10*x0)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = tmp1 > tmp0
    tmp20 = tl.full([1], 1, tl.int8)
    tmp21 = tl.full([1], 0, tl.int8)
    tmp22 = tl.where(tmp19, tmp20, tmp21)
    tmp23 = tmp3 > tmp2
    tmp24 = tl.full([1], 2, tl.int8)
    tmp25 = tl.where(tmp23, tmp24, tmp22)
    tmp26 = tmp5 > tmp4
    tmp27 = tl.full([1], 3, tl.int8)
    tmp28 = tl.where(tmp26, tmp27, tmp25)
    tmp29 = tmp7 > tmp6
    tmp30 = tl.full([1], 4, tl.int8)
    tmp31 = tl.where(tmp29, tmp30, tmp28)
    tmp32 = tmp9 > tmp8
    tmp33 = tl.full([1], 5, tl.int8)
    tmp34 = tl.where(tmp32, tmp33, tmp31)
    tmp35 = tmp11 > tmp10
    tmp36 = tl.full([1], 6, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp13 > tmp12
    tmp39 = tl.full([1], 7, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp15 > tmp14
    tmp42 = tl.full([1], 8, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp17 > tmp16
    tmp45 = tl.full([1], 9, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tl.store(out_ptr0 + (x0), tmp18, xmask)
    tl.store(out_ptr1 + (x0), tmp46, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jh/cjhhuvqv4jpwci3pjoye5g5d6rb6bmgur47aivil3d63wvznmwsr.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_3 => relu_1
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_5), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_1, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 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_relu_threshold_backward_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_relu_threshold_backward_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 100), (100, 100, 1))
    assert_size_stride(primals_2, (6, 1, 5), (5, 5, 1))
    assert_size_stride(primals_3, (6, ), (1, ))
    assert_size_stride(primals_4, (8, 60), (60, 1))
    assert_size_stride(primals_5, (8, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,), padding=(2,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 100), (600, 100, 1))
        buf1 = buf0; del buf0  # reuse
        buf7 = empty_strided_cuda((4, 6, 100), (600, 100, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv1d, relu], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0.run(buf1, primals_3, buf7, 2400, grid=grid(2400), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 6, 1, 10), (60, 10, 10, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 6, 1, 10), (60, 10, 10, 1), torch.int8)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf1, buf2, buf3, 240, grid=grid(240), stream=stream0)
        buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf2, (4, 60), (60, 1), 0), reinterpret_tensor(primals_4, (60, 8), (1, 60), 0), out=buf4)
        buf5 = buf4; del buf4  # reuse
        buf6 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_2.run(buf5, primals_5, buf6, 32, grid=grid(32), stream=stream0)
        del primals_5
    return (buf5, primals_2, primals_1, reinterpret_tensor(buf1, (4, 6, 1, 100), (600, 100, 100, 1), 0), buf3, reinterpret_tensor(buf2, (4, 60), (60, 1), 0), buf6, primals_4, buf7, )


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((4, 1, 100), (100, 100, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((6, 1, 5), (5, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((6, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((8, 60), (60, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
import torch.nn as nn
import torch.nn.functional as F


class SummaryNet(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=6, kernel_size=5,
            padding=2)
        self.pool = nn.MaxPool1d(kernel_size=10, stride=10)
        self.fc = nn.Linear(in_features=6 * 10, out_features=8)

    def forward(self, x):
        x = x.view(-1, 1, 100)
        x = self.pool(F.relu(self.conv1(x)))
        x = x.view(-1, 6 * 10)
        x = F.relu(self.fc(x))
        return x


def get_inputs():
    return [torch.rand([4, 1, 100])]


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
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_convolution_relu_threshold_backward_0(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 2400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 100 % 6
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 240
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 10 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 10 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 10 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 10 * x0), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (4 + 10 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (5 + 10 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (6 + 10 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + 10 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + 10 * x0), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + 10 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = tmp1 > tmp0
    tmp20 = tl.full([1], 1, tl.int8)
    tmp21 = tl.full([1], 0, tl.int8)
    tmp22 = tl.where(tmp19, tmp20, tmp21)
    tmp23 = tmp3 > tmp2
    tmp24 = tl.full([1], 2, tl.int8)
    tmp25 = tl.where(tmp23, tmp24, tmp22)
    tmp26 = tmp5 > tmp4
    tmp27 = tl.full([1], 3, tl.int8)
    tmp28 = tl.where(tmp26, tmp27, tmp25)
    tmp29 = tmp7 > tmp6
    tmp30 = tl.full([1], 4, tl.int8)
    tmp31 = tl.where(tmp29, tmp30, tmp28)
    tmp32 = tmp9 > tmp8
    tmp33 = tl.full([1], 5, tl.int8)
    tmp34 = tl.where(tmp32, tmp33, tmp31)
    tmp35 = tmp11 > tmp10
    tmp36 = tl.full([1], 6, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp13 > tmp12
    tmp39 = tl.full([1], 7, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp15 > tmp14
    tmp42 = tl.full([1], 8, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp17 > tmp16
    tmp45 = tl.full([1], 9, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tl.store(out_ptr0 + x0, tmp18, xmask)
    tl.store(out_ptr1 + x0, tmp46, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_2(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 100), (100, 100, 1))
    assert_size_stride(primals_2, (6, 1, 5), (5, 5, 1))
    assert_size_stride(primals_3, (6,), (1,))
    assert_size_stride(primals_4, (8, 60), (60, 1))
    assert_size_stride(primals_5, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(1,),
            padding=(2,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 100), (600, 100, 1))
        buf1 = buf0
        del buf0
        buf7 = empty_strided_cuda((4, 6, 100), (600, 100, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_threshold_backward_0[grid(2400)](buf1
            , primals_3, buf7, 2400, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 6, 1, 10), (60, 10, 10, 1), torch.float32
            )
        buf3 = empty_strided_cuda((4, 6, 1, 10), (60, 10, 10, 1), torch.int8)
        triton_poi_fused_max_pool2d_with_indices_1[grid(240)](buf1, buf2,
            buf3, 240, XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (4, 60), (60, 1), 0),
            reinterpret_tensor(primals_4, (60, 8), (1, 60), 0), out=buf4)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 8), (8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_2[grid(32)](buf5,
            primals_5, buf6, 32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_5
    return buf5, primals_2, primals_1, reinterpret_tensor(buf1, (4, 6, 1, 
        100), (600, 100, 100, 1), 0), buf3, reinterpret_tensor(buf2, (4, 60
        ), (60, 1), 0), buf6, primals_4, buf7


class SummaryNetNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv1d(in_channels=1, out_channels=6, kernel_size=5,
            padding=2)
        self.pool = nn.MaxPool1d(kernel_size=10, stride=10)
        self.fc = nn.Linear(in_features=6 * 10, out_features=8)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.fc.weight
        primals_5 = self.fc.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
