# AOT ID: ['0_inference']
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/sm/csmgx57ri3mpgapt57s7dinvbnt7h6cz7b4lqkjxpveoae5fnye4.py
# Topologically Sorted Source Nodes: [entity_states, max_1, sum_1, mean_pooled], Original ATen: [aten.mul, aten.max, aten.sum, aten.div]
# Source node to ATen node mapping:
#   entity_states => mul
#   max_1 => max_1
#   mean_pooled => div
#   sum_1 => sum_1
# Graph fragment:
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %unsqueeze_2), kwargs = {})
triton_poi_fused_div_max_mul_sum_0 = async_compile.triton('triton_poi_fused_div_max_mul_sum_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: '*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_div_max_mul_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_div_max_mul_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64) % 4
    x4 = (xindex // 256)
    x5 = xindex % 64
    x6 = xindex % 16
    x7 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (64*x3)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + (256*x4)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (4*x2) + (64*x3)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (64 + x5 + (256*x4)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + x0 + (4*x2) + (64*x3)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (128 + x5 + (256*x4)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x0 + (4*x2) + (64*x3)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (192 + x5 + (256*x4)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (x6 + (16*x7)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp16 = tmp14 / tmp15
    tmp17 = triton_helpers.maximum(tmp2, tmp5)
    tmp18 = triton_helpers.maximum(tmp17, tmp9)
    tmp19 = triton_helpers.maximum(tmp18, tmp13)
    tl.store(out_ptr0 + (x5 + (128*x7)), tmp16, xmask)
    tl.store(out_ptr1 + (x5 + (128*x7)), tmp19, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 8, 4, 4), (512, 128, 16, 4, 1), torch.float32)
        buf0 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 1), 64)  # alias
        buf1 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [entity_states, max_1, sum_1, mean_pooled], Original ATen: [aten.mul, aten.max, aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_max_mul_sum_0.run(arg0_1, arg1_1, arg2_1, buf0, buf1, 1024, grid=grid(1024), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    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
from torch import nn


class MeanMaxPooling(nn.Module):

    def __init__(self):
        super(MeanMaxPooling, self).__init__()

    def forward(self, doc_state, entity_mapping, entity_lens):
        """
        :param doc_state:  N x L x d
        :param entity_mapping:  N x E x L
        :param entity_lens:  N x E
        :return: N x E x 2d
        """
        entity_states = entity_mapping.unsqueeze(3) * doc_state.unsqueeze(1)
        max_pooled = torch.max(entity_states, dim=2)[0]
        mean_pooled = torch.sum(entity_states, dim=2) / entity_lens.unsqueeze(2
            )
        output = torch.cat([max_pooled, mean_pooled], dim=2)
        return output


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


def get_init_inputs():
    return [[], {}]

import torch
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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_div_max_mul_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64 % 4
    x4 = xindex // 256
    x5 = xindex % 64
    x6 = xindex % 16
    x7 = xindex // 64
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x5 + 256 * x4), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (64 + x5 + 256 * x4), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (128 + x5 + 256 * x4), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x0 + 4 * x2 + 64 * x3), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (192 + x5 + 256 * x4), xmask, eviction_policy
        ='evict_last')
    tmp15 = tl.load(in_ptr2 + (x6 + 16 * x7), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp16 = tmp14 / tmp15
    tmp17 = triton_helpers.maximum(tmp2, tmp5)
    tmp18 = triton_helpers.maximum(tmp17, tmp9)
    tmp19 = triton_helpers.maximum(tmp18, tmp13)
    tl.store(out_ptr0 + (x5 + 128 * x7), tmp16, xmask)
    tl.store(out_ptr1 + (x5 + 128 * x7), tmp19, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 8, 4, 4), (512, 128, 16, 4, 1),
            torch.float32)
        buf0 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 
            1), 64)
        buf1 = reinterpret_tensor(buf2, (4, 4, 4, 4, 4), (512, 128, 16, 4, 
            1), 0)
        get_raw_stream(0)
        triton_poi_fused_div_max_mul_sum_0[grid(1024)](arg0_1, arg1_1,
            arg2_1, buf0, buf1, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


class MeanMaxPoolingNew(nn.Module):

    def __init__(self):
        super(MeanMaxPoolingNew, self).__init__()

    def forward(self, input_0, input_1, input_2):
        arg1_1 = input_0
        arg0_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
