# 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_4/inductor_cache/ye/cyer3e3q24cnknj35g4jhiqyzqlzuynppjhp52ioic4qspesjovr.py
# Topologically Sorted Source Nodes: [masked_fill_, max_1, eq_1, masked_fill__1], Original ATen: [aten.masked_fill, aten.max, aten.eq]
# Source node to ATen node mapping:
#   eq_1 => eq_1
#   masked_fill_ => full_default, where
#   masked_fill__1 => full_default_1, where_1
#   max_1 => max_1
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -1000000.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%expand, %full_default, %arg1_1), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%where, 1), kwargs = {})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%getitem, -1000000.0), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %full_default_1, %getitem), kwargs = {})
triton_poi_fused_eq_masked_fill_max_0 = async_compile.triton('triton_poi_fused_eq_masked_fill_max_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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_eq_masked_fill_max_0', 'mutated_arg_names': ['in_out_ptr0'], '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_eq_masked_fill_max_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + (16*x1)), xmask)
    tmp6 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4 + x0 + (16*x1)), xmask)
    tmp11 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (8 + x0 + (16*x1)), xmask)
    tmp16 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (12 + x0 + (16*x1)), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = -1000000.0
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp6 == tmp1
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp12 = tmp11 == tmp1
    tmp14 = tl.where(tmp12, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp17 = tmp16 == tmp1
    tmp19 = tl.where(tmp17, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp20 == tmp4
    tmp22 = tl.where(tmp21, tmp1, tmp20)
    tl.store(in_out_ptr0 + (x2), tmp22, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [masked_fill_, max_1, eq_1, masked_fill__1], Original ATen: [aten.masked_fill, aten.max, aten.eq]
        stream0 = get_raw_stream(0)
        triton_poi_fused_eq_masked_fill_max_0.run(buf1, arg0_1, arg1_1, 16, grid=grid(16), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf1, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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
import torch.nn as nn


class MaxPooling(nn.Module):

    def __init__(self):
        super(MaxPooling, self).__init__()
        self.MIN = -1000000.0
    """
     (item, subitem) can be (word, characters), or (sentence, words)
     x: num_items x max_subitem_size x input_size
     x_mask: num_items x max_subitem_size
     return num_items x input_size
    """

    def forward(self, x, x_mask):
        """
         x_output: num_items x input_size x 1 --> num_items x input_size
        """
        empty_mask = x_mask.eq(0).unsqueeze(2).expand_as(x)
        x_now = x.clone()
        x_now.data.masked_fill_(empty_mask.data, self.MIN)
        x_output = x_now.max(1)[0]
        x_output.data.masked_fill_(x_output.data.eq(self.MIN), 0)
        return x_output


def get_inputs():
    return [torch.rand([4, 4, 4]), torch.rand([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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_eq_masked_fill_max_0(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask)
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = -1000000.0
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp6 == tmp1
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp12 = tmp11 == tmp1
    tmp14 = tl.where(tmp12, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp17 = tmp16 == tmp1
    tmp19 = tl.where(tmp17, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp20 == tmp4
    tmp22 = tl.where(tmp21, tmp1, tmp20)
    tl.store(in_out_ptr0 + x2, tmp22, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_eq_masked_fill_max_0[grid(16)](buf1, arg0_1,
            arg1_1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return buf1,


class MaxPoolingNew(nn.Module):

    def __init__(self):
        super(MaxPoolingNew, self).__init__()
        self.MIN = -1000000.0
    """
     (item, subitem) can be (word, characters), or (sentence, words)
     x: num_items x max_subitem_size x input_size
     x_mask: num_items x max_subitem_size
     return num_items x input_size
    """

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