# 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/uk/cukxpxu3y2z3ockiurz3auo6fuabuxyidhlinysaxu22bvje56hl.py
# Topologically Sorted Source Nodes: [mul, add_2, sub_1], Original ATen: [aten.mul, aten.add, aten.sub]
# Source node to ATen node mapping:
#   add_2 => add_2
#   mul => mul
#   sub_1 => sub_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %view_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %view_1), kwargs = {})
triton_poi_fused_add_mul_sub_0 = async_compile.triton('triton_poi_fused_add_mul_sub_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_sub_0', '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_add_mul_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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 % 4
    x2 = (xindex // 16)
    x3 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x3), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + (0))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp3 = 1e-08
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp3 - tmp5
    tmp7 = tl_math.log(tmp6)
    tmp8 = -tmp7
    tmp9 = tmp1 + tmp8
    tmp12 = tmp9 / tmp11
    tmp13 = tl.sigmoid(tmp12)
    tmp14 = tmp0 * tmp13
    tmp15 = 1.0
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 - tmp13
    tl.store(out_ptr0 + (x4), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/25/c25daqto32eti46zoqo3oi4plwezv2f3emcnmorgtd7ez3nnjfw4.py
# Topologically Sorted Source Nodes: [prod], Original ATen: [aten.prod]
# Source node to ATen node mapping:
#   prod => prod
# Graph fragment:
#   %prod : [num_users=1] = call_function[target=torch.ops.aten.prod.dim_int](args = (%sub_1, -2), kwargs = {})
triton_poi_fused_prod_1 = async_compile.triton('triton_poi_fused_prod_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=[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_prod_1', '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_prod_1(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
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tl.store(out_ptr0 + (x0), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ai/caimbi34x5fh62r7pvtxllp4bzj3d6tfgnrr536u5d6cfwqdnodb.py
# Topologically Sorted Source Nodes: [add, log, sub, log_1, gumbel, add_1, truediv, W], Original ATen: [aten.add, aten.log, aten.rsub, aten.neg, aten.div, aten.sigmoid, aten.sigmoid_backward]
# Source node to ATen node mapping:
#   W => sigmoid
#   add => add
#   add_1 => add_1
#   gumbel => neg
#   log => log
#   log_1 => log_1
#   sub => sub
#   truediv => div
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%rand, 1e-08), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1e-08, %log), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%log_1,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %neg), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, %primals_3), kwargs = {})
#   %sigmoid : [num_users=3] = call_function[target=torch.ops.aten.sigmoid.default](args = (%div,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %sub_2), kwargs = {})
triton_poi_fused_add_div_log_neg_rsub_sigmoid_sigmoid_backward_2 = async_compile.triton('triton_poi_fused_add_div_log_neg_rsub_sigmoid_sigmoid_backward_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: '*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_add_div_log_neg_rsub_sigmoid_sigmoid_backward_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 3, '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_add_div_log_neg_rsub_sigmoid_sigmoid_backward_2(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp9 = tl.load(in_ptr1 + (0))
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp2 = 1e-08
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tmp5 = tmp2 - tmp4
    tmp6 = tl_math.log(tmp5)
    tmp7 = -tmp6
    tmp8 = tmp0 + tmp7
    tmp11 = tmp8 / tmp10
    tmp12 = tl.sigmoid(tmp11)
    tmp13 = 1.0
    tmp14 = tmp13 - tmp12
    tmp15 = tmp12 * tmp14
    tl.store(in_out_ptr0 + (x0), tmp15, 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, 1), (4, 1, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (), ())
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [rand], Original ATen: [aten.rand]
        buf0 = torch.ops.aten.rand.default([4, 4], device=device(type='cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [mul, add_2, sub_1], Original ATen: [aten.mul, aten.add, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_sub_0.run(primals_1, primals_2, buf1, primals_3, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prod], Original ATen: [aten.prod]
        triton_poi_fused_prod_1.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [add, log, sub, log_1, gumbel, add_1, truediv, W], Original ATen: [aten.add, aten.log, aten.rsub, aten.neg, aten.div, aten.sigmoid, aten.sigmoid_backward]
        triton_poi_fused_add_div_log_neg_rsub_sigmoid_sigmoid_backward_2.run(buf4, primals_2, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_2
    return (buf3, primals_1, primals_3, buf2, buf4, )


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, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((), (), 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 collections
import torch
import torch.utils.data


def mnac(x, W, mode='prod'):
    out_size, in_size = W.size()
    x = x.view(x.size()[0], in_size, 1)
    W = W.t().view(1, in_size, out_size)
    if mode == 'prod':
        return torch.prod(x * W + 1 - W, -2)
    elif mode == 'exp-log':
        return torch.exp(torch.sum(torch.log(x * W + 1 - W), -2))
    elif mode == 'no-idendity':
        return torch.prod(x * W, -2)
    else:
        raise ValueError(f'mnac mode "{mode}" is not implemented')


class SummaryWriterNamespaceNoLoggingScope:

    def __init__(self, writer):
        self._writer = writer

    def __enter__(self):
        self._writer._logging_enabled = False

    def __exit__(self, type, value, traceback):
        self._writer._logging_enabled = True
        return False


class DummySummaryWriter:

    def __init__(self, **kwargs):
        self._logging_enabled = False
        pass

    def add_scalar(self, name, value, verbose_only=True):
        pass

    def add_summary(self, name, tensor, verbose_only=True):
        pass

    def add_histogram(self, name, tensor, verbose_only=True):
        pass

    def add_tensor(self, name, tensor, verbose_only=True):
        pass

    def print(self, name, tensor, verbose_only=True):
        pass

    def namespace(self, name):
        return self

    def every(self, epoch_interval):
        return self

    def verbose(self, verbose):
        return self

    def no_logging(self):
        return SummaryWriterNamespaceNoLoggingScope(self)


class NoRandomScope:

    def __init__(self, module):
        self._module = module

    def __enter__(self):
        self._module._disable_random()

    def __exit__(self, type, value, traceback):
        self._module._enable_random()
        return False


class ExtendedTorchModule(torch.nn.Module):

    def __init__(self, default_name, *args, writer=None, name=None, **kwargs):
        super().__init__()
        if writer is None:
            writer = DummySummaryWriter()
        self.writer = writer.namespace(default_name if name is None else name)
        self.allow_random = True

    def set_parameter(self, name, value):
        parameter = getattr(self, name, None)
        if isinstance(parameter, torch.nn.Parameter):
            parameter.fill_(value)
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.set_parameter(name, value)

    def regualizer(self, merge_in=None):
        regualizers = collections.defaultdict(int)
        if merge_in is not None:
            for key, value in merge_in.items():
                self.writer.add_scalar(f'regualizer/{key}', value)
                regualizers[key] += value
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                for key, value in module.regualizer().items():
                    regualizers[key] += value
        return regualizers

    def optimize(self, loss):
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.optimize(loss)

    def log_gradients(self):
        for name, parameter in self.named_parameters(recurse=False):
            if parameter.requires_grad:
                gradient, *_ = parameter.grad.data
                self.writer.add_summary(f'{name}/grad', gradient)
                self.writer.add_histogram(f'{name}/grad', gradient)
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.log_gradients()

    def no_internal_logging(self):
        return self.writer.no_logging()

    def _disable_random(self):
        self.allow_random = False
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module._disable_random()

    def _enable_random(self):
        self.allow_random = True
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module._enable_random()

    def no_random(self):
        return NoRandomScope(self)


class GumbelMNACLayer(ExtendedTorchModule):
    """Implements the NAC (Neural Accumulator)

    Arguments:
        in_features: number of ingoing features
        out_features: number of outgoing features
    """

    def __init__(self, in_features, out_features, **kwargs):
        super().__init__('nac', **kwargs)
        self.in_features = in_features
        self.out_features = out_features
        self.tau = torch.nn.Parameter(torch.tensor(1, dtype=torch.float32),
            requires_grad=False)
        self.register_buffer('target_weights', torch.tensor([1, -1, 0],
            dtype=torch.float32))
        self.U = torch.Tensor(out_features, in_features, 3)
        self.W_hat = torch.nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.register_parameter('bias', None)

    def reset_parameters(self):
        torch.nn.init.constant_(self.W_hat, 0)
        torch.nn.init.constant_(self.tau, 1)

    def forward(self, x, reuse=False):
        if self.allow_random:
            gumbel = -torch.log(1e-08 - torch.log(torch.rand(self.
                out_features, self.in_features, device=x.device) + 1e-08))
            W = torch.sigmoid((self.W_hat + gumbel) / self.tau)
        else:
            W = torch.sigmoid(self.W_hat)
        expected_W = torch.sigmoid(self.W_hat)
        self.writer.add_histogram('W', expected_W)
        self.writer.add_tensor('W', expected_W, verbose_only=False)
        return mnac(x, W)

    def extra_repr(self):
        return 'in_features={}, out_features={}'.format(self.in_features,
            self.out_features)


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


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

import torch
from torch import device
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.triton_helpers import math as tl_math
import collections
import torch.utils.data
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_add_mul_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    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 % 4
    x2 = xindex // 16
    x3 = xindex % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x3, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr3 + 0)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp3 = 1e-08
    tmp4 = tmp2 + tmp3
    tmp5 = tl_math.log(tmp4)
    tmp6 = tmp3 - tmp5
    tmp7 = tl_math.log(tmp6)
    tmp8 = -tmp7
    tmp9 = tmp1 + tmp8
    tmp12 = tmp9 / tmp11
    tmp13 = tl.sigmoid(tmp12)
    tmp14 = tmp0 * tmp13
    tmp15 = 1.0
    tmp16 = tmp14 + tmp15
    tmp17 = tmp16 - tmp13
    tl.store(out_ptr0 + x4, tmp17, xmask)


@triton.jit
def triton_poi_fused_prod_1(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
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_log_neg_rsub_sigmoid_sigmoid_backward_2(
    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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_out_ptr0 + x0, xmask)
    tmp9 = tl.load(in_ptr1 + 0)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp2 = 1e-08
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tmp5 = tmp2 - tmp4
    tmp6 = tl_math.log(tmp5)
    tmp7 = -tmp6
    tmp8 = tmp0 + tmp7
    tmp11 = tmp8 / tmp10
    tmp12 = tl.sigmoid(tmp11)
    tmp13 = 1.0
    tmp14 = tmp13 - tmp12
    tmp15 = tmp12 * tmp14
    tl.store(in_out_ptr0 + x0, tmp15, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (), ())
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.rand.default([4, 4], device=device(type=
            'cuda', index=0), pin_memory=False)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_sub_0[grid(64)](primals_1, primals_2, buf1,
            primals_3, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_prod_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf4 = buf1
        del buf1
        triton_poi_fused_add_div_log_neg_rsub_sigmoid_sigmoid_backward_2[grid
            (16)](buf4, primals_2, primals_3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_2
    return buf3, primals_1, primals_3, buf2, buf4


def mnac(x, W, mode='prod'):
    out_size, in_size = W.size()
    x = x.view(x.size()[0], in_size, 1)
    W = W.t().view(1, in_size, out_size)
    if mode == 'prod':
        return torch.prod(x * W + 1 - W, -2)
    elif mode == 'exp-log':
        return torch.exp(torch.sum(torch.log(x * W + 1 - W), -2))
    elif mode == 'no-idendity':
        return torch.prod(x * W, -2)
    else:
        raise ValueError(f'mnac mode "{mode}" is not implemented')


class SummaryWriterNamespaceNoLoggingScope:

    def __init__(self, writer):
        self._writer = writer

    def __enter__(self):
        self._writer._logging_enabled = False

    def __exit__(self, type, value, traceback):
        self._writer._logging_enabled = True
        return False


class DummySummaryWriter:

    def __init__(self, **kwargs):
        self._logging_enabled = False
        pass

    def add_scalar(self, name, value, verbose_only=True):
        pass

    def add_summary(self, name, tensor, verbose_only=True):
        pass

    def add_histogram(self, name, tensor, verbose_only=True):
        pass

    def add_tensor(self, name, tensor, verbose_only=True):
        pass

    def print(self, name, tensor, verbose_only=True):
        pass

    def namespace(self, name):
        return self

    def every(self, epoch_interval):
        return self

    def verbose(self, verbose):
        return self

    def no_logging(self):
        return SummaryWriterNamespaceNoLoggingScope(self)


class NoRandomScope:

    def __init__(self, module):
        self._module = module

    def __enter__(self):
        self._module._disable_random()

    def __exit__(self, type, value, traceback):
        self._module._enable_random()
        return False


class ExtendedTorchModule(torch.nn.Module):

    def __init__(self, default_name, *args, writer=None, name=None, **kwargs):
        super().__init__()
        if writer is None:
            writer = DummySummaryWriter()
        self.writer = writer.namespace(default_name if name is None else name)
        self.allow_random = True

    def set_parameter(self, name, value):
        parameter = getattr(self, name, None)
        if isinstance(parameter, torch.nn.Parameter):
            parameter.fill_(value)
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.set_parameter(name, value)

    def regualizer(self, merge_in=None):
        regualizers = collections.defaultdict(int)
        if merge_in is not None:
            for key, value in merge_in.items():
                self.writer.add_scalar(f'regualizer/{key}', value)
                regualizers[key] += value
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                for key, value in module.regualizer().items():
                    regualizers[key] += value
        return regualizers

    def optimize(self, loss):
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.optimize(loss)

    def log_gradients(self):
        for name, parameter in self.named_parameters(recurse=False):
            if parameter.requires_grad:
                gradient, *_ = parameter.grad.data
                self.writer.add_summary(f'{name}/grad', gradient)
                self.writer.add_histogram(f'{name}/grad', gradient)
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.log_gradients()

    def no_internal_logging(self):
        return self.writer.no_logging()

    def _disable_random(self):
        self.allow_random = False
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module._disable_random()

    def _enable_random(self):
        self.allow_random = True
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module._enable_random()

    def no_random(self):
        return NoRandomScope(self)


class GumbelMNACLayerNew(ExtendedTorchModule):
    """Implements the NAC (Neural Accumulator)

    Arguments:
        in_features: number of ingoing features
        out_features: number of outgoing features
    """

    def __init__(self, in_features, out_features, **kwargs):
        super().__init__('nac', **kwargs)
        self.in_features = in_features
        self.out_features = out_features
        self.tau = torch.nn.Parameter(torch.tensor(1, dtype=torch.float32),
            requires_grad=False)
        self.register_buffer('target_weights', torch.tensor([1, -1, 0],
            dtype=torch.float32))
        self.U = torch.Tensor(out_features, in_features, 3)
        self.W_hat = torch.nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.register_parameter('bias', None)

    def reset_parameters(self):
        torch.nn.init.constant_(self.W_hat, 0)
        torch.nn.init.constant_(self.tau, 1)

    def extra_repr(self):
        return 'in_features={}, out_features={}'.format(self.in_features,
            self.out_features)

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