# 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/bm/cbm6tfyfbvibw2xrkq2kd7b6hr2lb7vsiarwrnwzu3dpny6acnhb.py
# Topologically Sorted Source Nodes: [add, add_1, G], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   G => tanh
#   add => add
#   add_1 => add_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %unsqueeze), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %unsqueeze_1), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_tanh_0 = async_compile.triton('triton_poi_fused_add_tanh_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: '*fp32', 5: '*fp32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 6, '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_add_tanh_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp4 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x2), xmask)
    tmp8 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tl.store(in_out_ptr0 + (x2), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mc/cmczcqleqvynpeitxpozsz7si5a5pzlhofcqqm4cfrnysouzl3tw.py
# Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, sum_1, add_2], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.sum, aten.add]
# Source node to ATen node mapping:
#   add_2 => add_2
#   e_x => exp
#   e_x_1 => mul_1
#   max_1 => max_1
#   sub => sub
#   sum_1 => sum_1
#   x => mul
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_4, %primals_12), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 1, True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %primals_12), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1], True), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-06), kwargs = {})
triton_poi_fused_add_exp_max_mul_sub_sum_1 = async_compile.triton('triton_poi_fused_add_exp_max_mul_sub_sum_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: '*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_add_exp_max_mul_sub_sum_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_add_exp_max_mul_sub_sum_1(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
    x3 = (xindex // 4)
    x4 = xindex % 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp0 * tmp3
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tmp0 * tmp6
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp13 * tmp1
    tmp15 = tmp4 - tmp11
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 * tmp3
    tmp18 = tmp14 + tmp17
    tmp19 = tmp7 - tmp11
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 * tmp6
    tmp22 = tmp18 + tmp21
    tmp23 = tmp10 - tmp11
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp24 * tmp9
    tmp26 = tmp22 + tmp25
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tl.store(out_ptr0 + (x5), tmp11, xmask)
    tl.store(out_ptr1 + (x5), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jz/cjz2ly2sfrbducoq4qen3topgalw5xptx3mrfsbmnrixwpb75wx3.py
# Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, sum_1, add_2, softmax], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   add_2 => add_2
#   e_x => exp
#   e_x_1 => mul_1
#   max_1 => max_1
#   softmax => div
#   sub => sub
#   sum_1 => sum_1
#   x => mul
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute_4, %primals_12), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 1, True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %primals_12), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1], True), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add_2), kwargs = {})
triton_poi_fused_add_div_exp_max_mul_sub_sum_2 = async_compile.triton('triton_poi_fused_add_div_exp_max_mul_sub_sum_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=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_add_div_exp_max_mul_sub_sum_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_add_div_exp_max_mul_sub_sum_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
    x1 = (xindex // 4) % 16
    x3 = (xindex // 256)
    x4 = xindex % 256
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + (x5 + (64*x3)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 * tmp1
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x6), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (1, 4), (4, 1))
    assert_size_stride(primals_11, (1, ), (1, ))
    assert_size_stride(primals_12, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = reinterpret_tensor(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, add_1, G], Original ATen: [aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_tanh_0.run(buf3, primals_2, buf1, primals_5, buf2, primals_8, 256, grid=grid(256), stream=stream0)
        del primals_2
        del primals_5
        del primals_8
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_11
        buf6 = reinterpret_tensor(buf2, (4, 1, 4, 4, 4), (64, 256, 16, 4, 1), 0); del buf2  # reuse
        buf7 = reinterpret_tensor(buf1, (4, 1, 4, 4, 4), (64, 256, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, sum_1, add_2], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.sum, aten.add]
        triton_poi_fused_add_exp_max_mul_sub_sum_1.run(buf5, primals_12, buf6, buf7, 256, grid=grid(256), stream=stream0)
        buf8 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x, max_1, sub, e_x, e_x_1, sum_1, add_2, softmax], Original ATen: [aten.mul, aten.max, aten.sub, aten.exp, aten.sum, aten.add, aten.div]
        triton_poi_fused_add_div_exp_max_mul_sub_sum_2.run(buf5, primals_12, buf6, buf7, buf8, 1024, grid=grid(1024), stream=stream0)
        del buf6
        del buf7
    return (buf8, primals_12, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0), buf3, buf5, primals_10, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((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)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = 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, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11, primals_12])
    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
import torch.nn.functional as F


def masked_softmax(x, m=None, dim=-1):
    """
    Softmax with mask
    :param x:
    :param m:
    :param dim:
    :return:
    """
    if m is not None:
        m = m.float()
        x = x * m
    e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
    if m is not None:
        e_x = e_x * m
    softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
    return softmax


class MatchRNNAttention(torch.nn.Module):
    """
    attention mechanism in match-rnn
    Args:
        - input_size: The number of expected features in the input Hp and Hq
        - hidden_size: The number of features in the hidden state Hr

    Inputs:
        Hpi(batch, input_size): a context word encoded
        Hq(question_len, batch, input_size): whole question encoded
        Hr_last(batch, hidden_size): last lstm hidden output

    Outputs:
        alpha(batch, question_len): attention vector
    """

    def __init__(self, hp_input_size, hq_input_size, hidden_size):
        super(MatchRNNAttention, self).__init__()
        self.linear_wq = torch.nn.Linear(hq_input_size, hidden_size)
        self.linear_wp = torch.nn.Linear(hp_input_size, hidden_size)
        self.linear_wr = torch.nn.Linear(hidden_size, hidden_size)
        self.linear_wg = torch.nn.Linear(hidden_size, 1)

    def forward(self, Hpi, Hq, Hr_last, Hq_mask):
        wq_hq = self.linear_wq(Hq)
        wp_hp = self.linear_wp(Hpi).unsqueeze(0)
        wr_hr = self.linear_wr(Hr_last).unsqueeze(0)
        G = F.tanh(wq_hq + wp_hp + wr_hr)
        wg_g = self.linear_wg(G).squeeze(2).transpose(0, 1)
        alpha = masked_softmax(wg_g, m=Hq_mask, dim=1)
        return alpha


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


def get_init_inputs():
    return [[], {'hp_input_size': 4, 'hq_input_size': 4, 'hidden_size': 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 libdevice, math as tl_math
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_add_tanh_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x2, xmask)
    tmp8 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = libdevice.tanh(tmp10)
    tl.store(in_out_ptr0 + x2, tmp11, xmask)


@triton.jit
def triton_poi_fused_add_exp_max_mul_sub_sum_1(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
    x3 = xindex // 4
    x4 = xindex % 64
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (64 + x4), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (128 + x4), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (192 + x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp0 * tmp3
    tmp5 = triton_helpers.maximum(tmp2, tmp4)
    tmp7 = tmp0 * tmp6
    tmp8 = triton_helpers.maximum(tmp5, tmp7)
    tmp10 = tmp0 * tmp9
    tmp11 = triton_helpers.maximum(tmp8, tmp10)
    tmp12 = tmp2 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp13 * tmp1
    tmp15 = tmp4 - tmp11
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp16 * tmp3
    tmp18 = tmp14 + tmp17
    tmp19 = tmp7 - tmp11
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp20 * tmp6
    tmp22 = tmp18 + tmp21
    tmp23 = tmp10 - tmp11
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp24 * tmp9
    tmp26 = tmp22 + tmp25
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tl.store(out_ptr0 + x5, tmp11, xmask)
    tl.store(out_ptr1 + x5, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_div_exp_max_mul_sub_sum_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
    x1 = xindex // 4 % 16
    x3 = xindex // 256
    x4 = xindex % 256
    x5 = xindex % 64
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x1 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr3 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 * tmp1
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x6, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (1, 4), (4, 1))
    assert_size_stride(primals_11, (1,), (1,))
    assert_size_stride(primals_12, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = reinterpret_tensor(buf0, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1), 0
            )
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](buf3, primals_2, buf1,
            primals_5, buf2, primals_8, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_5
        del primals_8
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf3, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_11
        buf6 = reinterpret_tensor(buf2, (4, 1, 4, 4, 4), (64, 256, 16, 4, 1), 0
            )
        del buf2
        buf7 = reinterpret_tensor(buf1, (4, 1, 4, 4, 4), (64, 256, 16, 4, 1), 0
            )
        del buf1
        triton_poi_fused_add_exp_max_mul_sub_sum_1[grid(256)](buf5,
            primals_12, buf6, buf7, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused_add_div_exp_max_mul_sub_sum_2[grid(1024)](buf5,
            primals_12, buf6, buf7, buf8, 1024, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf6
        del buf7
    return buf8, primals_12, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0
        ), buf3, buf5, primals_10


def masked_softmax(x, m=None, dim=-1):
    """
    Softmax with mask
    :param x:
    :param m:
    :param dim:
    :return:
    """
    if m is not None:
        m = m.float()
        x = x * m
    e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
    if m is not None:
        e_x = e_x * m
    softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
    return softmax


class MatchRNNAttentionNew(torch.nn.Module):
    """
    attention mechanism in match-rnn
    Args:
        - input_size: The number of expected features in the input Hp and Hq
        - hidden_size: The number of features in the hidden state Hr

    Inputs:
        Hpi(batch, input_size): a context word encoded
        Hq(question_len, batch, input_size): whole question encoded
        Hr_last(batch, hidden_size): last lstm hidden output

    Outputs:
        alpha(batch, question_len): attention vector
    """

    def __init__(self, hp_input_size, hq_input_size, hidden_size):
        super(MatchRNNAttentionNew, self).__init__()
        self.linear_wq = torch.nn.Linear(hq_input_size, hidden_size)
        self.linear_wp = torch.nn.Linear(hp_input_size, hidden_size)
        self.linear_wr = torch.nn.Linear(hidden_size, hidden_size)
        self.linear_wg = torch.nn.Linear(hidden_size, 1)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_1 = self.linear_wq.weight
        primals_2 = self.linear_wq.bias
        primals_4 = self.linear_wp.weight
        primals_5 = self.linear_wp.bias
        primals_7 = self.linear_wr.weight
        primals_8 = self.linear_wr.bias
        primals_10 = self.linear_wg.weight
        primals_11 = self.linear_wg.bias
        primals_3 = input_0
        primals_6 = input_1
        primals_9 = input_2
        primals_12 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0]
