# 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/kd/ckdxxgoclliz5lzakuv2sbeywai7mrb5oztwl6f5ipzd4hqe42pt.py
# Topologically Sorted Source Nodes: [sigmoid, mul, x, output], Original ATen: [aten.sigmoid, aten.mul, aten.native_layer_norm]
# Source node to ATen node mapping:
#   mul => mul
#   output => var_mean
#   sigmoid => sigmoid
#   x => mul_1
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sigmoid), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 2.0), kwargs = {})
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%mul_1, [3]), kwargs = {correction: 0, keepdim: True})
triton_poi_fused_mul_native_layer_norm_sigmoid_0 = async_compile.triton('triton_poi_fused_mul_native_layer_norm_sigmoid_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: '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_mul_native_layer_norm_sigmoid_0', '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_mul_native_layer_norm_sigmoid_0(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 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (2))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp21 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (3))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp0 * tmp3
    tmp5 = 2.0
    tmp6 = tmp4 * tmp5
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp7 * tmp10
    tmp12 = tmp11 * tmp5
    tmp13 = tmp6 + tmp12
    tmp17 = tl.sigmoid(tmp16)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp18 * tmp5
    tmp20 = tmp13 + tmp19
    tmp24 = tl.sigmoid(tmp23)
    tmp25 = tmp21 * tmp24
    tmp26 = tmp25 * tmp5
    tmp27 = tmp20 + tmp26
    tmp28 = 4.0
    tmp29 = tmp27 / tmp28
    tmp30 = tmp6 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp12 - tmp29
    tmp33 = tmp32 * tmp32
    tmp34 = tmp31 + tmp33
    tmp35 = tmp19 - tmp29
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp38 = tmp26 - tmp29
    tmp39 = tmp38 * tmp38
    tmp40 = tmp37 + tmp39
    tmp41 = tmp40 / tmp28
    tl.store(out_ptr0 + (x0), tmp29, xmask)
    tl.store(out_ptr1 + (x0), tmp41, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xc/cxcpfundfbswf2vcixxfx2augodmjtu2z5yzl767pujn6nzsbm4e.py
# Topologically Sorted Source Nodes: [sigmoid, mul, x, output], Original ATen: [aten.sigmoid, aten.mul, aten.native_layer_norm]
# Source node to ATen node mapping:
#   mul => mul
#   output => add, add_1, mul_2, mul_3, rsqrt, sub
#   sigmoid => sigmoid
#   x => mul_1
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %sigmoid), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 2.0), kwargs = {})
#   %add : [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,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_1, %getitem_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_4), kwargs = {})
triton_poi_fused_mul_native_layer_norm_sigmoid_1 = async_compile.triton('triton_poi_fused_mul_native_layer_norm_sigmoid_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: '*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_mul_native_layer_norm_sigmoid_1', '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_mul_native_layer_norm_sigmoid_1(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
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr5 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tmp12 = tmp7 * tmp11
    tmp14 = tmp12 * tmp13
    tmp16 = tmp14 + tmp15
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul, x, output], Original ATen: [aten.sigmoid, aten.mul, aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_native_layer_norm_sigmoid_0.run(primals_2, primals_1, buf0, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul, x, output], Original ATen: [aten.sigmoid, aten.mul, aten.native_layer_norm]
        triton_poi_fused_mul_native_layer_norm_sigmoid_1.run(primals_2, primals_1, buf0, buf1, primals_3, primals_4, buf2, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf1
        del primals_4
    return (buf2, primals_1, primals_2, primals_3, )


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, ), (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)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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 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
import torch.nn.functional as F
import torch.nn as nn
import torch.functional as F
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_mul_native_layer_norm_sigmoid_0(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 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp7 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp21 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr1 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp0 * tmp3
    tmp5 = 2.0
    tmp6 = tmp4 * tmp5
    tmp10 = tl.sigmoid(tmp9)
    tmp11 = tmp7 * tmp10
    tmp12 = tmp11 * tmp5
    tmp13 = tmp6 + tmp12
    tmp17 = tl.sigmoid(tmp16)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp18 * tmp5
    tmp20 = tmp13 + tmp19
    tmp24 = tl.sigmoid(tmp23)
    tmp25 = tmp21 * tmp24
    tmp26 = tmp25 * tmp5
    tmp27 = tmp20 + tmp26
    tmp28 = 4.0
    tmp29 = tmp27 / tmp28
    tmp30 = tmp6 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = tmp12 - tmp29
    tmp33 = tmp32 * tmp32
    tmp34 = tmp31 + tmp33
    tmp35 = tmp19 - tmp29
    tmp36 = tmp35 * tmp35
    tmp37 = tmp34 + tmp36
    tmp38 = tmp26 - tmp29
    tmp39 = tmp38 * tmp38
    tmp40 = tmp37 + tmp39
    tmp41 = tmp40 / tmp28
    tl.store(out_ptr0 + x0, tmp29, xmask)
    tl.store(out_ptr1 + x0, tmp41, xmask)


@triton.jit
def triton_poi_fused_mul_native_layer_norm_sigmoid_1(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
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tmp4 = 2.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp5 - tmp6
    tmp9 = 1e-05
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tmp12 = tmp7 * tmp11
    tmp14 = tmp12 * tmp13
    tmp16 = tmp14 + tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_native_layer_norm_sigmoid_0[grid(64)](primals_2,
            primals_1, buf0, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_native_layer_norm_sigmoid_1[grid(256)](primals_2,
            primals_1, buf0, buf1, primals_3, primals_4, buf2, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del primals_4
    return buf2, primals_1, primals_2, primals_3


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 ResampleNormNew(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, input_0):
        primals_1 = self.mask
        primals_3 = self.norm.weight
        primals_4 = self.norm.bias
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
