# 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/hp/chpwcehtnvunea72lynrdl2354n7kd5im7izncef4zxh3hgjqcui.py
# Topologically Sorted Source Nodes: [features_test], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   features_test => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_3, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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=[64], 
    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_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zv/czvpfo7gqj3jium2inr5i7qioiijaa6e5yn7uhjgcjic3vk5adsy.py
# Topologically Sorted Source Nodes: [cosine_scores], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   cosine_scores => amax, exp, sub, sum_3
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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__softmax_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp5 = tmp1 * tmp4
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp1 * tmp7
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp1 * tmp10
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp3 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp5 - tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp8 - tmp12
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp12
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tl.store(out_ptr0 + (x0), tmp12, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ch/cchgkm4bkdcviz6qney2bnujdvljloavm3lff6gk7ruiijq74zm2.py
# Topologically Sorted Source Nodes: [cosine_scores], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   cosine_scores => amax, div_2, exp, sub, sum_3
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_3), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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__softmax_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5w/c5wyv3yj7aerxpxxcpkv2wzu7op2zvwpuaqblti7h47srmwpy4pa.py
# Topologically Sorted Source Nodes: [clamp, scores_cls_1], Original ATen: [aten.clamp, aten.log]
# Source node to ATen node mapping:
#   clamp => clamp_min_2
#   scores_cls_1 => log
# Graph fragment:
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%bmm_1, 1e-07), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min_2,), kwargs = {})
triton_poi_fused_clamp_log_3 = async_compile.triton('triton_poi_fused_clamp_log_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=[64], 
    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_clamp_log_3', '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_clamp_log_3(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 1e-07
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl_math.log(tmp2)
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [features_test], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(primals_3, buf0, 64, grid=grid(64), stream=stream0)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [features_train], Original ATen: [aten.div]
        triton_poi_fused_div_0.run(primals_1, buf1, 64, grid=grid(64), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [features_test, bmm], Original ATen: [aten.div, aten.bmm]
        extern_kernels.bmm(buf0, reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        buf4 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [cosine_scores], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(primals_4, buf2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        buf5 = reinterpret_tensor(buf1, (16, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [cosine_scores], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(primals_4, buf2, buf3, buf4, buf5, 64, grid=grid(64), stream=stream0)
        del buf3
        del buf4
        buf6 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [scores_cls], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0), primals_2, out=buf6)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [clamp, scores_cls_1], Original ATen: [aten.clamp, aten.log]
        triton_poi_fused_clamp_log_3.run(buf6, buf7, 64, grid=grid(64), stream=stream0)
    return (buf7, primals_4, buf2, buf6, reinterpret_tensor(primals_2, (4, 4, 4), (16, 1, 4), 0), )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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)

from _paritybench_helpers import _mock_config
import torch
import torch.nn as nn
import torch.optim
import torch.nn.functional as F
import torch.nn.parallel


class MatchingNetwork(nn.Module):

    def __init__(self, opt):
        super(MatchingNetwork, self).__init__()
        scale_cls = opt['scale_cls'] if 'scale_cls' in opt else 10.0
        self.scale_cls = nn.Parameter(torch.FloatTensor(1).fill_(scale_cls),
            requires_grad=True)

    def forward(self, features_test, features_train, labels_train):
        """Recognize novel categories based on the Matching Nets approach.

        Classify the test examples (i.e., `features_test`) using the available
        training examples (i.e., `features_test` and `labels_train`) using the
        Matching Nets approach.

        Args:
            features_test: A 3D tensor with shape
                [batch_size x num_test_examples x num_channels] that represents
                the test features of each training episode in the batch.
            features_train: A 3D tensor with shape
                [batch_size x num_train_examples x num_channels] that represents
                the train features of each training episode in the batch.
            labels_train: A 3D tensor with shape
                [batch_size x num_train_examples x nKnovel] that represents
                the train labels (encoded as 1-hot vectors) of each training
                episode in the batch.

        Return:
            scores_cls: A 3D tensor with shape
                [batch_size x num_test_examples x nKnovel] that represents the
                classification scores of the test feature vectors for the
                nKnovel novel categories.
        """
        assert features_train.dim() == 3
        assert labels_train.dim() == 3
        assert features_test.dim() == 3
        assert features_train.size(0) == labels_train.size(0)
        assert features_train.size(0) == features_test.size(0)
        assert features_train.size(1) == labels_train.size(1)
        assert features_train.size(2) == features_test.size(2)
        batch_size, num_test_examples, _num_channels = features_test.size()
        num_train_examples = features_train.size(1)
        labels_train.size(2)
        features_test = F.normalize(features_test, p=2, dim=features_test.
            dim() - 1, eps=1e-12)
        features_train = F.normalize(features_train, p=2, dim=
            features_train.dim() - 1, eps=1e-12)
        cosine_similarities = self.scale_cls * torch.bmm(features_test,
            features_train.transpose(1, 2))
        cosine_similarities = cosine_similarities.view(batch_size *
            num_test_examples, num_train_examples)
        cosine_scores = F.softmax(cosine_similarities)
        cosine_scores = cosine_scores.view(batch_size, num_test_examples,
            num_train_examples)
        scores_cls = torch.bmm(cosine_scores, labels_train)
        scores_cls = torch.log(torch.clamp(scores_cls, min=1e-07))
        return scores_cls


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


def get_init_inputs():
    return [[], {'opt': _mock_config(scale_cls=1.0)}]

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, math as tl_math
import torch.nn as nn
import torch.optim
import torch.nn.parallel
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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp1 * tmp2
    tmp5 = tmp1 * tmp4
    tmp6 = triton_helpers.maximum(tmp3, tmp5)
    tmp8 = tmp1 * tmp7
    tmp9 = triton_helpers.maximum(tmp6, tmp8)
    tmp11 = tmp1 * tmp10
    tmp12 = triton_helpers.maximum(tmp9, tmp11)
    tmp13 = tmp3 - tmp12
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp5 - tmp12
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp8 - tmp12
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp11 - tmp12
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tl.store(out_ptr0 + x0, tmp12, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clamp_log_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1e-07
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tl_math.log(tmp2)
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(64)](primals_3, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_div_0[grid(64)](primals_1, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf0, reinterpret_tensor(buf1, (4, 4, 4), (16, 1,
            4), 0), out=buf2)
        buf3 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        buf4 = empty_strided_cuda((16, 1), (1, 16), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](primals_4, buf2, buf3, buf4, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf1, (16, 4), (4, 1), 0)
        del buf1
        triton_poi_fused__softmax_2[grid(64)](primals_4, buf2, buf3, buf4,
            buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf3
        del buf4
        buf6 = buf0
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 
            0), primals_2, out=buf6)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)
        del buf5
        triton_poi_fused_clamp_log_3[grid(64)](buf6, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf7, primals_4, buf2, buf6, reinterpret_tensor(primals_2, (4, 4,
        4), (16, 1, 4), 0)


class MatchingNetworkNew(nn.Module):

    def __init__(self, opt):
        super(MatchingNetworkNew, self).__init__()
        scale_cls = opt['scale_cls'] if 'scale_cls' in opt else 10.0
        self.scale_cls = nn.Parameter(torch.FloatTensor(1).fill_(scale_cls),
            requires_grad=True)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.scale_cls
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
