# 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/ck/cck6zsxedo53nyj2po2pvkfjvrr75ansuu3rjjhu6zyrx6xzssqo.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.elu]
# Source node to ATen node mapping:
#   x_1 => expm1, gt, mul, mul_2, where
# Graph fragment:
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%view_1, 0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, 1.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expm1, 1.0), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %mul, %mul_2), kwargs = {})
triton_poi_fused_elu_0 = async_compile.triton('triton_poi_fused_elu_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=[256], 
    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_elu_0', 'mutated_arg_names': [], '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_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + (x0), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/g2/cg2n33ecqurwkyiyucsylguej6exc6zpz6fyhk7hcbdsevf2l4sr.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.glu]
# Source node to ATen node mapping:
#   x_5 => glu
# Graph fragment:
#   %glu : [num_users=2] = call_function[target=torch.ops.aten.glu.default](args = (%view_5,), kwargs = {})
triton_poi_fused_glu_1 = async_compile.triton('triton_poi_fused_glu_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=[256], 
    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_glu_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_glu_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 + (8*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + (8*x1)), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + (x2), tmp3, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ug/cug3tl2jlnwqshzkisx4mst6uxoyc5sgz3jeqwxup5r7eoieamdp.py
# Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   output => var_mean
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%glu, %primals_1), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add, [3]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_add_native_layer_norm_2 = async_compile.triton('triton_poi_fused_add_native_layer_norm_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_2', 'mutated_arg_names': [], '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_add_native_layer_norm_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + (x0), tmp16, xmask)
    tl.store(out_ptr1 + (x0), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uz/cuzkkvrgtcbz6vvu6omkkiofk54nlyp34lpauxhrah7fmpyygjuq.py
# Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   add => add
#   output => add_1, add_2, mul_3, mul_4, rsqrt, sub
# Graph fragment:
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%glu, %primals_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %getitem_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %primals_8), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %primals_9), kwargs = {})
triton_poi_fused_add_native_layer_norm_3 = async_compile.triton('triton_poi_fused_add_native_layer_norm_3', '''
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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '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, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_native_layer_norm_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_native_layer_norm_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (8, 4), (4, 1))
    assert_size_stride(primals_7, (8, ), (1, ))
    assert_size_stride(primals_8, (4, ), (1, ))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.elu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_elu_0.run(buf0, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, buf2, reinterpret_tensor(primals_6, (4, 8), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.glu]
        triton_poi_fused_glu_1.run(buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_2.run(buf4, primals_1, buf5, buf6, 64, grid=grid(64), stream=stream0)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, output], Original ATen: [aten.add, aten.native_layer_norm]
        triton_poi_fused_add_native_layer_norm_3.run(buf4, primals_1, buf5, buf6, primals_8, primals_9, buf7, 256, grid=grid(256), stream=stream0)
        del buf5
        del buf6
        del primals_9
    return (buf7, primals_1, primals_8, buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), buf2, reinterpret_tensor(buf3, (4, 4, 4, 8), (128, 32, 8, 1), 0), buf4, primals_6, primals_4, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((8, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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 torch import Tensor
from torch import nn as nn
import torch.nn.functional as F


class MonteCarloDropout(nn.Dropout):
    """
    Defines Monte Carlo dropout Module as defined
    in the paper https://arxiv.org/pdf/1506.02142.pdf.
    In summary, This technique uses the regular dropout
    which can be interpreted as a Bayesian approximation of
    a well-known probabilistic model: the Gaussian process.
    We can treat the many different networks
    (with different neurons dropped out) as Monte Carlo samples
    from the space of all available models. This provides mathematical
    grounds to reason about the model’s uncertainty and, as it turns out,
    often improves its performance.
    """
    mc_dropout_enabled: 'bool' = False

    def train(self, mode: 'bool'=True):
        if mode:
            self.mc_dropout_enabled = True

    def forward(self, input: 'Tensor') ->Tensor:
        return F.dropout(input, self.p, self.mc_dropout_enabled, self.inplace)


class _TimeDistributedInterpolation(nn.Module):

    def __init__(self, output_size: 'int', batch_first: 'bool'=False,
        trainable: 'bool'=False):
        super().__init__()
        self.output_size = output_size
        self.batch_first = batch_first
        self.trainable = trainable
        if self.trainable:
            self.mask = nn.Parameter(torch.zeros(self.output_size, dtype=
                torch.float32))
            self.gate = nn.Sigmoid()

    def interpolate(self, x):
        upsampled = F.interpolate(x.unsqueeze(1), self.output_size, mode=
            'linear', align_corners=True).squeeze(1)
        if self.trainable:
            upsampled = upsampled * self.gate(self.mask.unsqueeze(0)) * 2.0
        return upsampled

    def forward(self, x):
        if len(x.size()) <= 2:
            return self.interpolate(x)
        x_reshape = x.contiguous().view(-1, x.size(-1))
        y = self.interpolate(x_reshape)
        if self.batch_first:
            y = y.contiguous().view(x.size(0), -1, y.size(-1))
        else:
            y = y.view(-1, x.size(1), y.size(-1))
        return y


class _GatedLinearUnit(nn.Module):
    """Gated Linear Unit"""

    def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
        'float'=None):
        super().__init__()
        if dropout is not None:
            self.dropout = MonteCarloDropout(dropout)
        else:
            self.dropout = dropout
        self.hidden_size = hidden_size or input_size
        self.fc = nn.Linear(input_size, self.hidden_size * 2)
        self.init_weights()

    def init_weights(self):
        for n, p in self.named_parameters():
            if 'bias' in n:
                torch.nn.init.zeros_(p)
            elif 'fc' in n:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, x):
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.fc(x)
        x = F.glu(x, dim=-1)
        return x


class _ResampleNorm(nn.Module):

    def __init__(self, input_size: 'int', output_size: 'int'=None,
        trainable_add: 'bool'=True):
        super().__init__()
        self.input_size = input_size
        self.trainable_add = trainable_add
        self.output_size = output_size or input_size
        if self.input_size != self.output_size:
            self.resample = _TimeDistributedInterpolation(self.output_size,
                batch_first=True, trainable=False)
        if self.trainable_add:
            self.mask = nn.Parameter(torch.zeros(self.output_size, dtype=
                torch.float))
            self.gate = nn.Sigmoid()
        self.norm = nn.LayerNorm(self.output_size)

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        if self.input_size != self.output_size:
            x = self.resample(x)
        if self.trainable_add:
            x = x * self.gate(self.mask) * 2.0
        output = self.norm(x)
        return output


class _AddNorm(nn.Module):

    def __init__(self, input_size: 'int', skip_size: 'int'=None,
        trainable_add: 'bool'=True):
        super().__init__()
        self.input_size = input_size
        self.trainable_add = trainable_add
        self.skip_size = skip_size or input_size
        if self.input_size != self.skip_size:
            self.resample = _TimeDistributedInterpolation(self.input_size,
                batch_first=True, trainable=False)
        if self.trainable_add:
            self.mask = nn.Parameter(torch.zeros(self.input_size, dtype=
                torch.float))
            self.gate = nn.Sigmoid()
        self.norm = nn.LayerNorm(self.input_size)

    def forward(self, x: 'torch.Tensor', skip: 'torch.Tensor'):
        if self.input_size != self.skip_size:
            skip = self.resample(skip)
        if self.trainable_add:
            skip = skip * self.gate(self.mask) * 2.0
        output = self.norm(x + skip)
        return output


class _GateAddNorm(nn.Module):

    def __init__(self, input_size: 'int', hidden_size: 'int'=None,
        skip_size: 'int'=None, trainable_add: 'bool'=False, dropout:
        'float'=None):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size or input_size
        self.skip_size = skip_size or self.hidden_size
        self.dropout = dropout
        self.glu = _GatedLinearUnit(self.input_size, hidden_size=self.
            hidden_size, dropout=self.dropout)
        self.add_norm = _AddNorm(self.hidden_size, skip_size=self.skip_size,
            trainable_add=trainable_add)

    def forward(self, x, skip):
        output = self.glu(x)
        output = self.add_norm(output, skip)
        return output


class _GatedResidualNetwork(nn.Module):

    def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
        'int', dropout: 'float'=0.1, context_size: 'int'=None, residual:
        'bool'=False):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.context_size = context_size
        self.hidden_size = hidden_size
        self.dropout = dropout
        self.residual = residual
        if self.input_size != self.output_size and not self.residual:
            residual_size = self.input_size
        else:
            residual_size = self.output_size
        if self.output_size != residual_size:
            self.resample_norm = _ResampleNorm(residual_size, self.output_size)
        self.fc1 = nn.Linear(self.input_size, self.hidden_size)
        self.elu = nn.ELU()
        if self.context_size is not None:
            self.context = nn.Linear(self.context_size, self.hidden_size,
                bias=False)
        self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
        self.init_weights()
        self.gate_norm = _GateAddNorm(input_size=self.hidden_size,
            skip_size=self.output_size, hidden_size=self.output_size,
            dropout=self.dropout, trainable_add=False)

    def init_weights(self):
        for name, p in self.named_parameters():
            if 'bias' in name:
                torch.nn.init.zeros_(p)
            elif 'fc1' in name or 'fc2' in name:
                torch.nn.init.kaiming_normal_(p, a=0, mode='fan_in',
                    nonlinearity='leaky_relu')
            elif 'context' in name:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, x, context=None, residual=None):
        if residual is None:
            residual = x
        if self.input_size != self.output_size and not self.residual:
            residual = self.resample_norm(residual)
        x = self.fc1(x)
        if context is not None:
            context = self.context(context)
            x = x + context
        x = self.elu(x)
        x = self.fc2(x)
        x = self.gate_norm(x, residual)
        return x


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 4, 'output_size': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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 libdevice
from torch import Tensor
from torch import nn as nn
import torch.nn.functional as F
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_poi_fused_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_glu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_3(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (8, 4), (4, 1))
    assert_size_stride(primals_7, (8,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(256)](buf0, buf1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf2, reinterpret_tensor(primals_6,
            (4, 8), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_glu_1[grid(256)](buf3, buf4, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_add_native_layer_norm_2[grid(64)](buf4, primals_1,
            buf5, buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_3[grid(256)](buf4, primals_1,
            buf5, buf6, primals_8, primals_9, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf5
        del buf6
        del primals_9
    return buf7, primals_1, primals_8, buf0, reinterpret_tensor(buf1, (64, 
        4), (4, 1), 0), buf2, reinterpret_tensor(buf3, (4, 4, 4, 8), (128, 
        32, 8, 1), 0), buf4, primals_6, primals_4


class MonteCarloDropout(nn.Dropout):
    """
    Defines Monte Carlo dropout Module as defined
    in the paper https://arxiv.org/pdf/1506.02142.pdf.
    In summary, This technique uses the regular dropout
    which can be interpreted as a Bayesian approximation of
    a well-known probabilistic model: the Gaussian process.
    We can treat the many different networks
    (with different neurons dropped out) as Monte Carlo samples
    from the space of all available models. This provides mathematical
    grounds to reason about the model’s uncertainty and, as it turns out,
    often improves its performance.
    """
    mc_dropout_enabled: 'bool' = False

    def train(self, mode: 'bool'=True):
        if mode:
            self.mc_dropout_enabled = True

    def forward(self, input: 'Tensor') ->Tensor:
        return F.dropout(input, self.p, self.mc_dropout_enabled, self.inplace)


class _TimeDistributedInterpolation(nn.Module):

    def __init__(self, output_size: 'int', batch_first: 'bool'=False,
        trainable: 'bool'=False):
        super().__init__()
        self.output_size = output_size
        self.batch_first = batch_first
        self.trainable = trainable
        if self.trainable:
            self.mask = nn.Parameter(torch.zeros(self.output_size, dtype=
                torch.float32))
            self.gate = nn.Sigmoid()

    def interpolate(self, x):
        upsampled = F.interpolate(x.unsqueeze(1), self.output_size, mode=
            'linear', align_corners=True).squeeze(1)
        if self.trainable:
            upsampled = upsampled * self.gate(self.mask.unsqueeze(0)) * 2.0
        return upsampled

    def forward(self, x):
        if len(x.size()) <= 2:
            return self.interpolate(x)
        x_reshape = x.contiguous().view(-1, x.size(-1))
        y = self.interpolate(x_reshape)
        if self.batch_first:
            y = y.contiguous().view(x.size(0), -1, y.size(-1))
        else:
            y = y.view(-1, x.size(1), y.size(-1))
        return y


class _GatedLinearUnit(nn.Module):
    """Gated Linear Unit"""

    def __init__(self, input_size: 'int', hidden_size: 'int'=None, dropout:
        'float'=None):
        super().__init__()
        if dropout is not None:
            self.dropout = MonteCarloDropout(dropout)
        else:
            self.dropout = dropout
        self.hidden_size = hidden_size or input_size
        self.fc = nn.Linear(input_size, self.hidden_size * 2)
        self.init_weights()

    def init_weights(self):
        for n, p in self.named_parameters():
            if 'bias' in n:
                torch.nn.init.zeros_(p)
            elif 'fc' in n:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, x):
        if self.dropout is not None:
            x = self.dropout(x)
        x = self.fc(x)
        x = F.glu(x, dim=-1)
        return x


class _ResampleNorm(nn.Module):

    def __init__(self, input_size: 'int', output_size: 'int'=None,
        trainable_add: 'bool'=True):
        super().__init__()
        self.input_size = input_size
        self.trainable_add = trainable_add
        self.output_size = output_size or input_size
        if self.input_size != self.output_size:
            self.resample = _TimeDistributedInterpolation(self.output_size,
                batch_first=True, trainable=False)
        if self.trainable_add:
            self.mask = nn.Parameter(torch.zeros(self.output_size, dtype=
                torch.float))
            self.gate = nn.Sigmoid()
        self.norm = nn.LayerNorm(self.output_size)

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        if self.input_size != self.output_size:
            x = self.resample(x)
        if self.trainable_add:
            x = x * self.gate(self.mask) * 2.0
        output = self.norm(x)
        return output


class _AddNorm(nn.Module):

    def __init__(self, input_size: 'int', skip_size: 'int'=None,
        trainable_add: 'bool'=True):
        super().__init__()
        self.input_size = input_size
        self.trainable_add = trainable_add
        self.skip_size = skip_size or input_size
        if self.input_size != self.skip_size:
            self.resample = _TimeDistributedInterpolation(self.input_size,
                batch_first=True, trainable=False)
        if self.trainable_add:
            self.mask = nn.Parameter(torch.zeros(self.input_size, dtype=
                torch.float))
            self.gate = nn.Sigmoid()
        self.norm = nn.LayerNorm(self.input_size)

    def forward(self, x: 'torch.Tensor', skip: 'torch.Tensor'):
        if self.input_size != self.skip_size:
            skip = self.resample(skip)
        if self.trainable_add:
            skip = skip * self.gate(self.mask) * 2.0
        output = self.norm(x + skip)
        return output


class _GateAddNorm(nn.Module):

    def __init__(self, input_size: 'int', hidden_size: 'int'=None,
        skip_size: 'int'=None, trainable_add: 'bool'=False, dropout:
        'float'=None):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size or input_size
        self.skip_size = skip_size or self.hidden_size
        self.dropout = dropout
        self.glu = _GatedLinearUnit(self.input_size, hidden_size=self.
            hidden_size, dropout=self.dropout)
        self.add_norm = _AddNorm(self.hidden_size, skip_size=self.skip_size,
            trainable_add=trainable_add)

    def forward(self, x, skip):
        output = self.glu(x)
        output = self.add_norm(output, skip)
        return output


class _GatedResidualNetworkNew(nn.Module):

    def __init__(self, input_size: 'int', hidden_size: 'int', output_size:
        'int', dropout: 'float'=0.1, context_size: 'int'=None, residual:
        'bool'=False):
        super().__init__()
        self.input_size = input_size
        self.output_size = output_size
        self.context_size = context_size
        self.hidden_size = hidden_size
        self.dropout = dropout
        self.residual = residual
        if self.input_size != self.output_size and not self.residual:
            residual_size = self.input_size
        else:
            residual_size = self.output_size
        if self.output_size != residual_size:
            self.resample_norm = _ResampleNorm(residual_size, self.output_size)
        self.fc1 = nn.Linear(self.input_size, self.hidden_size)
        self.elu = nn.ELU()
        if self.context_size is not None:
            self.context = nn.Linear(self.context_size, self.hidden_size,
                bias=False)
        self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
        self.init_weights()
        self.gate_norm = _GateAddNorm(input_size=self.hidden_size,
            skip_size=self.output_size, hidden_size=self.output_size,
            dropout=self.dropout, trainable_add=False)

    def init_weights(self):
        for name, p in self.named_parameters():
            if 'bias' in name:
                torch.nn.init.zeros_(p)
            elif 'fc1' in name or 'fc2' in name:
                torch.nn.init.kaiming_normal_(p, a=0, mode='fan_in',
                    nonlinearity='leaky_relu')
            elif 'context' in name:
                torch.nn.init.xavier_uniform_(p)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.gate_norm.glu.fc.weight
        primals_7 = self.gate_norm.glu.fc.bias
        primals_8 = self.gate_norm.add_norm.norm.weight
        primals_9 = self.gate_norm.add_norm.norm.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
