# 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_7/inductor_cache/7l/c7lvig2zhj2iehtujvqrkzrlwayqdobfawdm2rzdswlxaokpz36v.py
# Topologically Sorted Source Nodes: [setitem], Original ATen: [aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   setitem => full_default_1, index_put
# Graph fragment:
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -inf), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%arg0_1, [%full_default], %full_default_1), 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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), 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': '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_index_put_lift_fresh_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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 = tl.full([1], False, tl.int1)
    tmp2 = float("-inf")
    tmp3 = tl.where(tmp1, tmp2, tmp0)
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/kd/ckdjqudl6o2qnmvu7ql4ktibvn34h6thuyobkjyytvrpbzhegrn5.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_3, index_put_1
# Graph fragment:
#   %full_default_3 : [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_1 : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%index_put, [%bitwise_not_1], %full_default_3), 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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), 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': '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_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp5 = tl.load(in_ptr0 + (x0), xmask)
    tmp0 = tl.full([1], True, tl.int1)
    tmp1 = tmp0 | tmp0
    tmp2 = tmp1 | tmp0
    tmp3 = tmp2 | tmp0
    tmp4 = tmp3 == 0
    tmp6 = 0.0
    tmp7 = tl.where(tmp4, tmp6, tmp5)
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5l/c5lg2i2ofxflslf7bu6w7i56fb72x6ezjgb35d2woe4gnj5ur6ao.py
# Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => getitem
# Graph fragment:
#   %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%max_1, 0), kwargs = {})
triton_poi_fused_max_2 = async_compile.triton('triton_poi_fused_max_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_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_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 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(arg0_1, arg0_1, 64, grid=grid(64), 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(arg0_1, arg0_1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_1], Original ATen: [aten.max]
        triton_poi_fused_max_2.run(arg0_1, buf2, 16, grid=grid(16), stream=stream0)
        del arg0_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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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 typing import Optional
import torch.utils.data
import torch.nn


class MaskedTemporalPooling(torch.nn.Module):
    """
    Applies temporal pooling operations on masked inputs. For each pooling operation
    all masked values are ignored.
    """

    def __init__(self, method: 'str'):
        """
        method (str): the method of pooling to use. Options:
            'max': reduces temporal dimension to each valid max value.
            'avg': averages valid values in the temporal dimension.
            'sum': sums valid values in the temporal dimension.
            Note if all batch row elements are invalid, the temporal dimension is
            pooled to 0 values.
        """
        super().__init__()
        assert method in ('max', 'avg', 'sum')
        self._method = method

    def forward(self, x: 'torch.Tensor', mask: 'Optional[torch.Tensor]'=None
        ) ->torch.Tensor:
        """
        Args:
            x (torch.Tensor): tensor with shape (batch_size, seq_len, feature_dim)
            mask (torch.Tensor): bool tensor with shape (batch_size, seq_len).
                Sequence elements that are False are invalid.

        Returns:
            Tensor with shape (batch_size, feature_dim)
        """
        assert x.dim(
            ) == 3, 'Requires x shape (batch_size x seq_len x feature_dim)'
        b, t = x.shape[0], x.shape[1]
        if mask is None:
            mask = torch.ones((b, t), dtype=torch.bool)
        if self._method == 'max':
            x[~mask, :] = float('-inf')
            invalid_first_dim = ~mask.view(b, -1).any(dim=-1)
            x[invalid_first_dim, :] = 0
            x = torch.max(x, dim=1)[0]
        elif self._method == 'avg':
            x = x * mask.unsqueeze(-1).float()
            mask = mask.view(b, t, -1).any(dim=-1)
            valid_lengths = mask.float().sum(dim=-1).int()
            x = x.sum(dim=1)
            x = x.div(valid_lengths.clamp(min=1).unsqueeze(-1).expand(x.
                size()).float())
        elif self._method == 'sum':
            x = x * mask.unsqueeze(-1).float()
            x = x.sum(dim=1)
        else:
            raise NotImplementedError(
                f"{self._method} not available options are: 'max', 'avg', 'sum'"
                )
        return x


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


def get_init_inputs():
    return [[], {'method': 'max'}]

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.utils.data
import torch.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 = 64
    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 = tl.full([1], False, tl.int1)
    tmp2 = float('-inf')
    tmp3 = tl.where(tmp1, tmp2, tmp0)
    tl.store(out_ptr0 + x0, tmp3, xmask)


@triton.jit
def triton_poi_fused_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp5 = tl.load(in_ptr0 + x0, xmask)
    tmp0 = tl.full([1], True, tl.int1)
    tmp1 = tmp0 | tmp0
    tmp2 = tmp1 | tmp0
    tmp3 = tmp2 | tmp0
    tmp4 = tmp3 == 0
    tmp6 = 0.0
    tmp7 = tl.where(tmp4, tmp6, tmp5)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        get_raw_stream(0)
        triton_poi_fused_index_put_lift_fresh_0[grid(64)](arg0_1, arg0_1, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        triton_poi_fused_index_put_lift_fresh_1[grid(64)](arg0_1, arg0_1, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_max_2[grid(16)](arg0_1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
    return buf2,


class MaskedTemporalPoolingNew(torch.nn.Module):
    """
    Applies temporal pooling operations on masked inputs. For each pooling operation
    all masked values are ignored.
    """

    def __init__(self, method: 'str'):
        """
        method (str): the method of pooling to use. Options:
            'max': reduces temporal dimension to each valid max value.
            'avg': averages valid values in the temporal dimension.
            'sum': sums valid values in the temporal dimension.
            Note if all batch row elements are invalid, the temporal dimension is
            pooled to 0 values.
        """
        super().__init__()
        assert method in ('max', 'avg', 'sum')
        self._method = method

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