# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_1/inductor_cache/cj/ccjrbiivtgszjm4ak34pn3m4vzzp72pz7gxawg6fztwj7ljqvt3r.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, %unsqueeze_3), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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=[4096], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), '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_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x7 = (xindex // 64)
    x4 = (xindex // 256) % 4
    x5 = (xindex // 1024)
    x6 = (xindex // 4) % 16
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64) % 4
    x1 = (xindex // 4) % 4
    x9 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x7)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x6 + (16*x5) + (64*x4)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x2 + (4*x5) + (16*x4) + (64*x3)), None, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = tmp3 / tmp5
    tmp7 = x1
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp6 * tmp8
    tmp10 = tmp0 + tmp9
    tl.store(out_ptr0 + (x9), tmp10, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4), (4, 16, 1024, 256, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(arg2_1, arg0_1, arg1_1, buf0, 4096, grid=grid(4096), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from torch import nn


class TimeEncoding(nn.Module):

    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(TimeEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x, mask, lengths):
        time = mask * 1 / (lengths[..., None] - 1)
        time = time[:, None] * torch.arange(time.shape[1], device=x.device)[
            None, :]
        time = time[:, 0].T
        x = x + time[..., None]
        return self.dropout(x)


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


def get_init_inputs():
    return [[], {'d_model': 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 import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 4
    x7 = xindex // 64
    x4 = xindex // 256 % 4
    x5 = xindex // 1024
    x6 = xindex // 4 % 16
    x2 = xindex // 16 % 4
    x3 = xindex // 64 % 4
    x1 = xindex // 4 % 4
    x9 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x7), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x6 + 16 * x5 + 64 * x4), None,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x2 + 4 * x5 + 16 * x4 + 64 * x3), None,
        eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp4 - tmp2
    tmp6 = tmp3 / tmp5
    tmp7 = x1
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp6 * tmp8
    tmp10 = tmp0 + tmp9
    tl.store(out_ptr0 + x9, tmp10, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4, 4), (4, 16, 1024, 256, 64,
            1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(4096)](arg2_1, arg0_1, arg1_1, buf0, 
            4096, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf0,


class TimeEncodingNew(nn.Module):

    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super(TimeEncodingNew, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
