# 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/xw/cxwrk2fodabatoqbpxcl5eb2ifidbtfcttbbx5hhgzol35c2myr4.py
# Topologically Sorted Source Nodes: [simmat], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   simmat => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_1, [0, 3, 0, 3], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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=[1024], 
    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_constant_pad_nd_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 7) % 7
    x0 = xindex % 7
    x2 = (xindex // 49)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ch/cchl3etcboj5jw4xggmrhxdnvzhxcggabe4regsbgagybypkwnfy.py
# Topologically Sorted Source Nodes: [conv2d, conv, max_1], Original ATen: [aten.convolution, aten.relu, aten.max]
# Source node to ATen node mapping:
#   conv => relu
#   conv2d => convolution
#   max_1 => max_1
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [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 = {})
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.dim](args = (%relu, 1), kwargs = {})
triton_poi_fused_convolution_max_relu_1 = async_compile.triton('triton_poi_fused_convolution_max_relu_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 3: '*fp32', 4: '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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_max_relu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_max_relu_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (1))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp26 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp27 = tl.load(in_ptr1 + (2))
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
    tmp45 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp46 = tl.load(in_ptr1 + (3))
    tmp47 = tl.broadcast_to(tmp46, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp9 = tmp6 + tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = tmp5 > tmp10
    tmp12 = tmp5 == tmp10
    tmp13 = tmp5 != tmp5
    tmp14 = tmp10 != tmp10
    tmp15 = tmp13 > tmp14
    tmp16 = tmp11 | tmp15
    tmp17 = tmp13 & tmp14
    tmp18 = tmp12 | tmp17
    tmp19 = tl.full([1], 0, tl.int64)
    tmp20 = tl.full([1], 1, tl.int64)
    tmp21 = tmp19 < tmp20
    tmp22 = tmp18 & tmp21
    tmp23 = tmp16 | tmp22
    tmp24 = tl.where(tmp23, tmp5, tmp10)
    tmp25 = tl.where(tmp23, tmp19, tmp20)
    tmp29 = tmp26 + tmp28
    tmp30 = triton_helpers.maximum(tmp4, tmp29)
    tmp31 = tmp24 > tmp30
    tmp32 = tmp24 == tmp30
    tmp33 = tmp24 != tmp24
    tmp34 = tmp30 != tmp30
    tmp35 = tmp33 > tmp34
    tmp36 = tmp31 | tmp35
    tmp37 = tmp33 & tmp34
    tmp38 = tmp32 | tmp37
    tmp39 = tl.full([1], 2, tl.int64)
    tmp40 = tmp25 < tmp39
    tmp41 = tmp38 & tmp40
    tmp42 = tmp36 | tmp41
    tmp43 = tl.where(tmp42, tmp24, tmp30)
    tmp44 = tl.where(tmp42, tmp25, tmp39)
    tmp48 = tmp45 + tmp47
    tmp49 = triton_helpers.maximum(tmp4, tmp48)
    tmp50 = tmp43 > tmp49
    tmp51 = tmp43 == tmp49
    tmp52 = tmp43 != tmp43
    tmp53 = tmp49 != tmp49
    tmp54 = tmp52 > tmp53
    tmp55 = tmp50 | tmp54
    tmp56 = tmp52 & tmp53
    tmp57 = tmp51 | tmp56
    tmp58 = tl.full([1], 3, tl.int64)
    tmp59 = tmp44 < tmp58
    tmp60 = tmp57 & tmp59
    tmp61 = tmp55 | tmp60
    tmp62 = tl.where(tmp61, tmp43, tmp49)
    tmp63 = tl.where(tmp61, tmp44, tmp58)
    tmp64 = triton_helpers.maximum(tmp5, tmp10)
    tmp65 = triton_helpers.maximum(tmp64, tmp30)
    tmp66 = triton_helpers.maximum(tmp65, tmp49)
    tl.store(out_ptr0 + (x2), tmp63, xmask)
    tl.store(out_ptr1 + (x2), tmp66, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/35/c35vxy5oullorpkglb324ij376h6o5mbjsqjoykh7yiskiyinasn.py
# Topologically Sorted Source Nodes: [conv2d, conv], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv => relu
#   conv2d => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%constant_pad_nd, %primals_2, %primals_3, [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 = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_2 = async_compile.triton('triton_poi_fused_convolution_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=[256], 
    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_2', '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_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 7), (196, 49, 7, 1), torch.float32)
        # Topologically Sorted Source Nodes: [simmat], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_1, buf0, 784, grid=grid(784), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, conv, max_1], Original ATen: [aten.convolution, aten.relu, aten.max]
        triton_poi_fused_convolution_max_relu_1.run(buf1, primals_3, buf2, buf3, 64, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d, conv, max_1, topk], Original ATen: [aten.convolution, aten.relu, aten.max, aten.topk]
        buf4 = torch.ops.aten.topk.default(buf3, 4, 2)
        del buf3
        buf5 = buf4[0]
        buf6 = buf4[1]
        del buf4
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d, conv], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_2.run(buf1, primals_3, buf7, 256, grid=grid(256), stream=stream0)
        del buf1
        del primals_3
    return (buf5, primals_2, buf0, buf6, reinterpret_tensor(buf2, (4, 1, 4, 4), (16, 16, 4, 1), 0), 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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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.utils.data


class PACRRConvMax2dModule(torch.nn.Module):

    def __init__(self, shape, n_filters, k, channels):
        super().__init__()
        self.shape = shape
        if shape != 1:
            self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
        else:
            self.pad = None
        self.conv = torch.nn.Conv2d(channels, n_filters, shape)
        self.activation = torch.nn.ReLU()
        self.k = k
        self.shape = shape
        self.channels = channels

    def forward(self, simmat):
        BATCH, _CHANNELS, QLEN, _DLEN = simmat.shape
        if self.pad:
            simmat = self.pad(simmat)
        conv = self.activation(self.conv(simmat))
        top_filters, _ = conv.max(dim=1)
        top_toks, _ = top_filters.topk(self.k, dim=2)
        result = top_toks.reshape(BATCH, QLEN, self.k)
        return result


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


def get_init_inputs():
    return [[], {'shape': 4, 'n_filters': 4, 'k': 4, 'channels': 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
from torch._inductor.runtime import triton_helpers
import torch.utils.data
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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 784
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 7 % 7
    x0 = xindex % 7
    x2 = xindex // 49
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_max_relu_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + 1)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp26 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr1 + 2)
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
    tmp45 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp46 = tl.load(in_ptr1 + 3)
    tmp47 = tl.broadcast_to(tmp46, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp9 = tmp6 + tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = tmp5 > tmp10
    tmp12 = tmp5 == tmp10
    tmp13 = tmp5 != tmp5
    tmp14 = tmp10 != tmp10
    tmp15 = tmp13 > tmp14
    tmp16 = tmp11 | tmp15
    tmp17 = tmp13 & tmp14
    tmp18 = tmp12 | tmp17
    tmp19 = tl.full([1], 0, tl.int64)
    tmp20 = tl.full([1], 1, tl.int64)
    tmp21 = tmp19 < tmp20
    tmp22 = tmp18 & tmp21
    tmp23 = tmp16 | tmp22
    tmp24 = tl.where(tmp23, tmp5, tmp10)
    tmp25 = tl.where(tmp23, tmp19, tmp20)
    tmp29 = tmp26 + tmp28
    tmp30 = triton_helpers.maximum(tmp4, tmp29)
    tmp31 = tmp24 > tmp30
    tmp32 = tmp24 == tmp30
    tmp33 = tmp24 != tmp24
    tmp34 = tmp30 != tmp30
    tmp35 = tmp33 > tmp34
    tmp36 = tmp31 | tmp35
    tmp37 = tmp33 & tmp34
    tmp38 = tmp32 | tmp37
    tmp39 = tl.full([1], 2, tl.int64)
    tmp40 = tmp25 < tmp39
    tmp41 = tmp38 & tmp40
    tmp42 = tmp36 | tmp41
    tmp43 = tl.where(tmp42, tmp24, tmp30)
    tmp44 = tl.where(tmp42, tmp25, tmp39)
    tmp48 = tmp45 + tmp47
    tmp49 = triton_helpers.maximum(tmp4, tmp48)
    tmp50 = tmp43 > tmp49
    tmp51 = tmp43 == tmp49
    tmp52 = tmp43 != tmp43
    tmp53 = tmp49 != tmp49
    tmp54 = tmp52 > tmp53
    tmp55 = tmp50 | tmp54
    tmp56 = tmp52 & tmp53
    tmp57 = tmp51 | tmp56
    tmp58 = tl.full([1], 3, tl.int64)
    tmp59 = tmp44 < tmp58
    tmp60 = tmp57 & tmp59
    tmp61 = tmp55 | tmp60
    tl.where(tmp61, tmp43, tmp49)
    tmp63 = tl.where(tmp61, tmp44, tmp58)
    tmp64 = triton_helpers.maximum(tmp5, tmp10)
    tmp65 = triton_helpers.maximum(tmp64, tmp30)
    tmp66 = triton_helpers.maximum(tmp65, tmp49)
    tl.store(out_ptr0 + x2, tmp63, xmask)
    tl.store(out_ptr1 + x2, tmp66, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + x3, tmp6, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 7, 7), (196, 49, 7, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(784)](primals_1, buf0, 784,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 4, 4), (64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_max_relu_1[grid(64)](buf1, primals_3,
            buf2, buf3, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf4 = torch.ops.aten.topk.default(buf3, 4, 2)
        del buf3
        buf5 = buf4[0]
        buf6 = buf4[1]
        del buf4
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_2[grid(256)](buf1,
            primals_3, buf7, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_3
    return buf5, primals_2, buf0, buf6, reinterpret_tensor(buf2, (4, 1, 4, 
        4), (16, 16, 4, 1), 0), buf7


class PACRRConvMax2dModuleNew(torch.nn.Module):

    def __init__(self, shape, n_filters, k, channels):
        super().__init__()
        self.shape = shape
        if shape != 1:
            self.pad = torch.nn.ConstantPad2d((0, shape - 1, 0, shape - 1), 0)
        else:
            self.pad = None
        self.conv = torch.nn.Conv2d(channels, n_filters, shape)
        self.activation = torch.nn.ReLU()
        self.k = k
        self.shape = shape
        self.channels = channels

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