# 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_1/inductor_cache/vz/cvzt73yoz5pfmchwdwsny5uv5jyurfobzhfy6lmuk7awqb2vi25z.py
# Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   alpha_1 => add
#   alphas => amax, exp, sub_1, sum_1
#   mul => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %unsqueeze), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -1e+30), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %mul), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
triton_poi_fused__softmax_add_mul_rsub_0 = async_compile.triton('triton_poi_fused__softmax_add_mul_rsub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_add_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = (xindex // 4) % 16
    x3 = (xindex // 64)
    x5 = xindex % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (16 + x4), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (16 + x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (32 + x4), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (32 + x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (48 + x4), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (48 + x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp9 = tmp2 - tmp8
    tmp10 = tmp9 * tmp4
    tmp11 = tmp7 + tmp10
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp15 = tmp2 - tmp14
    tmp16 = tmp15 * tmp4
    tmp17 = tmp13 + tmp16
    tmp18 = triton_helpers.maximum(tmp12, tmp17)
    tmp21 = tmp2 - tmp20
    tmp22 = tmp21 * tmp4
    tmp23 = tmp19 + tmp22
    tmp24 = triton_helpers.maximum(tmp18, tmp23)
    tmp25 = tmp6 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp11 - tmp24
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = tmp17 - tmp24
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tmp33 = tmp23 - tmp24
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp32 + tmp34
    tl.store(out_ptr0 + (x6), tmp24, xmask)
    tl.store(out_ptr1 + (x6), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/6j/c6jsf26ee3wt3gr3i6hx55reluznyaxx5ayb57rqgnmfaslb2765.py
# Topologically Sorted Source Nodes: [pooled_x], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   pooled_x => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64) % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x5), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/qj/cqjndrubvc4xamedkkoqsnvlfkg4vff3xkbascmhq4dvxsosvgpg.py
# Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   alpha_1 => add
#   alphas => amax, div, exp, sub_1
#   mul => mul
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %unsqueeze), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, -1e+30), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_2, %mul), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_mul_rsub_2 = async_compile.triton('triton_poi_fused__softmax_add_mul_rsub_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_add_mul_rsub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_mul_rsub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x5 = (xindex // 4) % 64
    x6 = xindex % 16
    x7 = (xindex // 64)
    x4 = (xindex // 256)
    x8 = xindex % 64
    x9 = xindex
    tmp0 = tl.load(in_ptr0 + (x5), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x6 + (16*x7)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (x8 + (64*x4)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (x8 + (64*x4)), xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(out_ptr0 + (x9), tmp11, 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, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_rsub_0.run(buf0, primals_3, buf1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pooled_x], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(primals_2, buf3, 1024, grid=grid(1024), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, mul, alpha_1, alphas], Original ATen: [aten.rsub, aten.mul, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_rsub_2.run(buf0, primals_3, buf1, buf2, buf4, 1024, grid=grid(1024), stream=stream0)
        del buf1
        del buf2
        buf5 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pooled_x], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (64, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf4, (64, 4, 4), (16, 4, 1), 0), out=buf5)
        del buf4
    return (reinterpret_tensor(buf5, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0), primals_3, buf0, reinterpret_tensor(buf3, (64, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_2, (4, 64), (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, 1), (1, 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, 4, 4, 4), (64, 16, 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.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class WeightedPool(nn.Module):

    def __init__(self, dim):
        super(WeightedPool, self).__init__()
        weight = torch.empty(dim, 1)
        nn.init.xavier_uniform_(weight)
        self.weight = nn.Parameter(weight, requires_grad=True)

    def forward(self, x, mask):
        alpha = torch.tensordot(x, self.weight, dims=1)
        alpha = mask_logits(alpha, mask=mask.unsqueeze(2))
        alphas = nn.Softmax(dim=1)(alpha)
        pooled_x = torch.matmul(x.transpose(1, 2), alphas)
        pooled_x = pooled_x.squeeze(2)
        return pooled_x


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


def get_init_inputs():
    return [[], {'dim': 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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.parallel
import torch.nn as nn
import torch.utils.data
import torch.backends.cudnn
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__softmax_add_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex // 4 % 16
    x3 = xindex // 64
    x5 = xindex % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (16 + x4), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (16 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (32 + x4), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (32 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (48 + x4), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (48 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp9 = tmp2 - tmp8
    tmp10 = tmp9 * tmp4
    tmp11 = tmp7 + tmp10
    tmp12 = triton_helpers.maximum(tmp6, tmp11)
    tmp15 = tmp2 - tmp14
    tmp16 = tmp15 * tmp4
    tmp17 = tmp13 + tmp16
    tmp18 = triton_helpers.maximum(tmp12, tmp17)
    tmp21 = tmp2 - tmp20
    tmp22 = tmp21 * tmp4
    tmp23 = tmp19 + tmp22
    tmp24 = triton_helpers.maximum(tmp18, tmp23)
    tmp25 = tmp6 - tmp24
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp11 - tmp24
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = tmp17 - tmp24
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tmp33 = tmp23 - tmp24
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp32 + tmp34
    tl.store(out_ptr0 + x6, tmp24, xmask)
    tl.store(out_ptr1 + x6, tmp35, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64 % 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + x5, tmp0, xmask)


@triton.jit
def triton_poi_fused__softmax_add_mul_rsub_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x5 = xindex // 4 % 64
    x6 = xindex % 16
    x7 = xindex // 64
    x4 = xindex // 256
    x8 = xindex % 64
    x9 = xindex
    tmp0 = tl.load(in_ptr0 + x5, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x6 + 16 * x7), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr2 + (x8 + 64 * x4), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr3 + (x8 + 64 * x4), xmask, eviction_policy=
        'evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp6 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tmp9 / tmp10
    tl.store(out_ptr0 + x9, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 16, 4, 1),
            torch.float32)
        buf2 = empty_strided_cuda((4, 1, 4, 4, 4), (64, 256, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_add_mul_rsub_0[grid(256)](buf0, primals_3,
            buf1, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_clone_1[grid(1024)](primals_2, buf3, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused__softmax_add_mul_rsub_2[grid(1024)](buf0,
            primals_3, buf1, buf2, buf4, 1024, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf1
        del buf2
        buf5 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf4, (64, 4, 4), (16, 4, 1), 0), out=buf5)
        del buf4
    return reinterpret_tensor(buf5, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
        ), primals_3, buf0, reinterpret_tensor(buf3, (64, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)


def mask_logits(inputs, mask, mask_value=-1e+30):
    mask = mask.type(torch.float32)
    return inputs + (1.0 - mask) * mask_value


class WeightedPoolNew(nn.Module):

    def __init__(self, dim):
        super(WeightedPoolNew, self).__init__()
        weight = torch.empty(dim, 1)
        nn.init.xavier_uniform_(weight)
        self.weight = nn.Parameter(weight, requires_grad=True)

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