# 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/hn/chnistd2faczwsk64bvezdfjqgkyl4bhbywvkzr4fqobzqsym5oe.py
# Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem => full_default, index_put
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 2.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%arg1_1, [%ge], %full_default), kwargs = {})
triton_poi_fused_index_put_lift_fresh_0 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_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=[128], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_0', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], '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_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 100
    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 = 0.9
    tmp2 = tmp0 >= tmp1
    tmp3 = 2.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/7k/c7kuenuo3moffnjkrnfbdcmwtf7a6y4dof4leolzdk5k4wyq23zr.py
# Topologically Sorted Source Nodes: [setitem_1], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem_1 => full_default_1, index_put_1
# Graph fragment:
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_1 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%lt], %full_default_1), kwargs = {})
triton_poi_fused_index_put_lift_fresh_1 = async_compile.triton('triton_poi_fused_index_put_lift_fresh_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=[128], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_put_lift_fresh_1', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], '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_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 100
    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 = 0.9
    tmp2 = tmp0 < tmp1
    tmp3 = 1.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + (x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/tm/ctmkwovs7mxdhxhyeeof5rgpvd6q5vmai3nkmdrtclnnpclea4wc.py
# Topologically Sorted Source Nodes: [setitem_2, mul, mul_1, sum_1, truediv], Original ATen: [aten.lift_fresh, aten.index_put, aten.mul, aten.sum, aten.div]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   setitem_2 => full_default_2, index_put_2
#   sum_1 => sum_1
#   truediv => div
# Graph fragment:
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put_2 : [num_users=3] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put_1, [%eq], %full_default_2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %index_put_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 100), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%index_put_2,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %sum_1), kwargs = {})
triton_per_fused_div_index_put_lift_fresh_mul_sum_2 = async_compile.triton('triton_per_fused_div_index_put_lift_fresh_mul_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.persistent_reduction(
    size_hints=[1, 128],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_index_put_lift_fresh_mul_sum_2', 'mutated_arg_names': ['in_ptr0', 'out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_index_put_lift_fresh_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 100
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0)
    tmp9 = tl.load(in_ptr1 + (r0), rmask, other=0.0)
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = 0.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tmp10 = tmp9 * tmp4
    tmp11 = 100.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 / tmp8
    tl.store(out_ptr0 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp4, rmask)
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp13, rmask)
''', 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, 1, 1, 25), (25, 25, 25, 1))
    assert_size_stride(arg1_1, (4, 1, 1, 25), (25, 25, 25, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
        stream0 = get_raw_stream(0)
        triton_poi_fused_index_put_lift_fresh_0.run(arg1_1, arg1_1, 100, grid=grid(100), stream=stream0)
        # Topologically Sorted Source Nodes: [setitem_1], Original ATen: [aten.lift_fresh, aten.index_put]
        triton_poi_fused_index_put_lift_fresh_1.run(arg1_1, arg1_1, 100, grid=grid(100), stream=stream0)
        buf7 = empty_strided_cuda((4, 1, 1, 25), (25, 25, 25, 1), torch.float32)
        # Topologically Sorted Source Nodes: [setitem_2, mul, mul_1, sum_1, truediv], Original ATen: [aten.lift_fresh, aten.index_put, aten.mul, aten.sum, aten.div]
        triton_per_fused_div_index_put_lift_fresh_mul_sum_2.run(arg1_1, arg0_1, arg1_1, buf7, 1, 100, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf7, )


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, 1, 1, 25), (25, 25, 25, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 1, 1, 25), (25, 25, 25, 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 DropBlock_Ske(nn.Module):

    def __init__(self, num_point=25, keep_prob=0.9):
        super(DropBlock_Ske, self).__init__()
        self.keep_prob = keep_prob
        self.num_point = num_point

    def forward(self, input, mask):
        n, _c, _t, _v = input.size()
        mask[mask >= self.keep_prob] = 2.0
        mask[mask < self.keep_prob] = 1.0
        mask[mask == 2.0] = 0.0
        mask = mask.view(n, 1, 1, self.num_point)
        return input * mask * mask.numel() / mask.sum()


def get_inputs():
    return [torch.rand([4, 1, 1, 25]), torch.rand([4, 1, 1, 25])]


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
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_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 100
    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 = 0.9
    tmp2 = tmp0 >= tmp1
    tmp3 = 2.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 100
    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 = 0.9
    tmp2 = tmp0 < tmp1
    tmp3 = 1.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_per_fused_div_index_put_lift_fresh_mul_sum_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 100
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp9 = tl.load(in_ptr1 + r0, rmask, other=0.0)
    tmp1 = 2.0
    tmp2 = tmp0 == tmp1
    tmp3 = 0.0
    tmp4 = tl.where(tmp2, tmp3, tmp0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tmp10 = tmp9 * tmp4
    tmp11 = 100.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp12 / tmp8
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp4, rmask)
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp13, rmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 1, 1, 25), (25, 25, 25, 1))
    assert_size_stride(arg1_1, (4, 1, 1, 25), (25, 25, 25, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        get_raw_stream(0)
        triton_poi_fused_index_put_lift_fresh_0[grid(100)](arg1_1, arg1_1, 
            100, XBLOCK=128, num_warps=4, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_1[grid(100)](arg1_1, arg1_1, 
            100, XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 1, 1, 25), (25, 25, 25, 1), torch.float32
            )
        triton_per_fused_div_index_put_lift_fresh_mul_sum_2[grid(1)](arg1_1,
            arg0_1, arg1_1, buf7, 1, 100, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf7,


class DropBlock_SkeNew(nn.Module):

    def __init__(self, num_point=25, keep_prob=0.9):
        super(DropBlock_SkeNew, self).__init__()
        self.keep_prob = keep_prob
        self.num_point = num_point

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