# 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/kq/ckqpgjkefsaygrl3b3ktu2kqjnxztj4afoz6gn2iexdub7n3vrx2.py
# Topologically Sorted Source Nodes: [mask, float_1, mul, sub, mul_1, x], Original ATen: [aten.any, aten._to_copy, aten.mul, aten.rsub, aten.add]
# Source node to ATen node mapping:
#   float_1 => convert_element_type
#   mask => any_1
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
#   x => add
# Graph fragment:
#   %any_1 : [num_users=1] = call_function[target=torch.ops.aten.any.dim](args = (%view, -1), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%unsqueeze_2, torch.float32), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %convert_element_type), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %convert_element_type), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %sub), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_per_fused__to_copy_add_any_mul_rsub_0 = async_compile.triton('triton_per_fused__to_copy_add_any_mul_rsub_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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: '*fp32', 5: 'i32', 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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_add_any_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused__to_copy_add_any_mul_rsub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    r2 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + (r1 + (64*x0)), xmask, other=0.0)
    tmp9 = tl.load(in_ptr2 + (r2), None, eviction_policy='evict_last')
    tmp1 = (tmp0 != 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = triton_helpers.any(tmp4, 1)[:, None]
    tmp7 = tmp5.to(tl.float32)
    tmp8 = tmp6 * tmp7
    tmp10 = 1.0
    tmp11 = tmp10 - tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tl.store(out_ptr1 + (r1 + (64*x0)), tmp13, xmask)
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, ), (1, ), torch.bool)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mask, float_1, mul, sub, mul_1, x], Original ATen: [aten.any, aten._to_copy, aten.mul, aten.rsub, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_add_any_mul_rsub_0.run(primals_1, primals_2, primals_3, buf0, buf1, 4, 64, grid=grid(4), stream=stream0)
        del primals_1
        del primals_2
        del primals_3
    return (buf1, reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0), )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import torch.utils.data
import torch.nn


class LearnMaskedDefault(nn.Module):
    """
    Learns default values to fill invalid entries within input tensors. The
    invalid entries are represented by a mask which is passed into forward alongside
    the input tensor. Note the default value is only used if all entries in the batch row are
    invalid rather than just a portion of invalid entries within each batch row.
    """

    def __init__(self, feature_dim: 'int', init_method: 'str'='gaussian',
        freeze: 'bool'=False):
        """
        Args:
            feature_dim (int): the size of the default value parameter, this must match the
                input tensor size.
            init_method (str): the initial default value parameter. Options:
                'guassian'
                'zeros'
            freeze (bool): If True, the learned default parameter weights are frozen.
        """
        super().__init__()
        if init_method == 'zeros':
            self._learned_defaults = nn.Parameter(torch.zeros(feature_dim),
                requires_grad=not freeze)
        elif init_method == 'gaussian':
            self._learned_defaults = nn.Parameter(torch.Tensor(feature_dim),
                requires_grad=not freeze)
            nn.init.normal_(self._learned_defaults)
        else:
            raise NotImplementedError(
                f"{init_method} not available. Options are: 'zeros' or 'gaussian'"
                )

    def forward(self, x: 'torch.Tensor', mask: 'torch.Tensor') ->torch.Tensor:
        """
        Args:
            x (torch.Tensor): tensor of shape (batch_size, feature_dim).
            mask (torch.Tensor): bool tensor of shape (batch_size, seq_len) If all elements
                in the batch dimension are False the learned default parameter is used for
                that batch element.

        Returns:
            Tensor with shape (batch_size, feature_dim)
        """
        mask = mask.view(mask.shape[0], -1).any(dim=-1)
        for i in range(1, x.dim()):
            mask = mask.unsqueeze(i)
        x = x * mask.float() + self._learned_defaults * (1 - mask.float())
        return x


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


def get_init_inputs():
    return [[], {'feature_dim': 4}]

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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused__to_copy_add_any_mul_rsub_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    r2 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp6 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp9 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
    tmp1 = tmp0 != 0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = triton_helpers.any(tmp4, 1)[:, None]
    tmp7 = tmp5.to(tl.float32)
    tmp8 = tmp6 * tmp7
    tmp10 = 1.0
    tmp11 = tmp10 - tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tl.store(out_ptr1 + (r1 + 64 * x0), tmp13, xmask)
    tl.store(out_ptr0 + x0, tmp5, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.bool)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__to_copy_add_any_mul_rsub_0[grid(4)](primals_1,
            primals_2, primals_3, buf0, buf1, 4, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del primals_1
        del primals_2
        del primals_3
    return buf1, reinterpret_tensor(buf0, (4, 1, 1, 1), (1, 1, 1, 1), 0)


class LearnMaskedDefaultNew(nn.Module):
    """
    Learns default values to fill invalid entries within input tensors. The
    invalid entries are represented by a mask which is passed into forward alongside
    the input tensor. Note the default value is only used if all entries in the batch row are
    invalid rather than just a portion of invalid entries within each batch row.
    """

    def __init__(self, feature_dim: 'int', init_method: 'str'='gaussian',
        freeze: 'bool'=False):
        """
        Args:
            feature_dim (int): the size of the default value parameter, this must match the
                input tensor size.
            init_method (str): the initial default value parameter. Options:
                'guassian'
                'zeros'
            freeze (bool): If True, the learned default parameter weights are frozen.
        """
        super().__init__()
        if init_method == 'zeros':
            self._learned_defaults = nn.Parameter(torch.zeros(feature_dim),
                requires_grad=not freeze)
        elif init_method == 'gaussian':
            self._learned_defaults = nn.Parameter(torch.Tensor(feature_dim),
                requires_grad=not freeze)
            nn.init.normal_(self._learned_defaults)
        else:
            raise NotImplementedError(
                f"{init_method} not available. Options are: 'zeros' or 'gaussian'"
                )

    def forward(self, input_0, input_1):
        primals_3 = self._learned_defaults
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
