# 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_4/inductor_cache/55/c557jmlab6qu6pnzzhglt7obp2gs6irgiorb4my377v4j57ymzrr.py
# Topologically Sorted Source Nodes: [weights, mul, x, x_1], Original ATen: [aten.index, aten.mul, aten.sum, aten.sigmoid]
# Source node to ATen node mapping:
#   mul => mul
#   weights => index
#   x => sum_1
#   x_1 => sigmoid
# Graph fragment:
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%primals_1, [%primals_2]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index, %primals_3), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%sum_1,), kwargs = {})
triton_poi_fused_index_mul_sigmoid_sum_0 = async_compile.triton('triton_poi_fused_index_mul_sigmoid_sum_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: '*i64', 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_index_mul_sigmoid_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_index_mul_sigmoid_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4) % 4
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (x4 + (64*x2)), xmask)
    tmp9 = tl.load(in_ptr2 + (16 + x4 + (64*x2)), xmask)
    tmp12 = tl.load(in_ptr2 + (32 + x4 + (64*x2)), xmask)
    tmp15 = tl.load(in_ptr2 + (48 + x4 + (64*x2)), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (x0 + (4*tmp4)), xmask)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp6 * tmp9
    tmp11 = tmp8 + tmp10
    tmp13 = tmp6 * tmp12
    tmp14 = tmp11 + tmp13
    tmp16 = tmp6 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tl.sigmoid(tmp17)
    tl.store(out_ptr0 + (x5), tmp18, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (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), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [weights, mul, x, x_1], Original ATen: [aten.index, aten.mul, aten.sum, aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_index_mul_sigmoid_sum_0.run(primals_2, primals_1, primals_3, buf0, 64, grid=grid(64), stream=stream0)
        del primals_1
    return (buf0, primals_2, primals_3, buf0, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
from torch import nn


class Net(nn.Module):

    def __init__(self, input_size, output_size, num_emojis, dropout):
        super().__init__()
        self.V = torch.nn.Parameter(torch.empty(num_emojis, output_size).
            uniform_(-0.1, 0.1))
        self.dropout = torch.nn.Dropout(p=dropout)
        if not input_size == output_size:
            self.is_proj = True
            self.W = torch.nn.Parameter(torch.empty(input_size, output_size
                ).uniform_(-0.1, 0.1))
            self.tanh = torch.nn.Tanh()
        else:
            self.is_proj = False

    def forward(self, x, emoji_ids):
        if self.is_proj:
            proj = torch.mm(x, self.W)
            x = self.tanh(proj)
        weights = self.V[emoji_ids]
        weights = self.dropout(weights)
        x = torch.sum(torch.mul(weights, x), 1)
        x = torch.sigmoid(x)
        return x

    def project_embeddings(self, embeddings_array):
        return self.tanh(torch.mm(torch.Tensor(embeddings_array), self.W)
            ).detach().numpy()


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'input_size': 4, 'output_size': 4, 'num_emojis': 4,
        'dropout': 0.5}]

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_index_mul_sigmoid_sum_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex % 16
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (x4 + 64 * x2), xmask)
    tmp9 = tl.load(in_ptr2 + (16 + x4 + 64 * x2), xmask)
    tmp12 = tl.load(in_ptr2 + (32 + x4 + 64 * x2), xmask)
    tmp15 = tl.load(in_ptr2 + (48 + x4 + 64 * x2), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * tmp4), xmask)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp6 * tmp9
    tmp11 = tmp8 + tmp10
    tmp13 = tmp6 * tmp12
    tmp14 = tmp11 + tmp13
    tmp16 = tmp6 * tmp15
    tmp17 = tmp14 + tmp16
    tmp18 = tl.sigmoid(tmp17)
    tl.store(out_ptr0 + x5, tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_index_mul_sigmoid_sum_0[grid(64)](primals_2,
            primals_1, primals_3, buf0, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
    return buf0, primals_2, primals_3, buf0


class NetNew(nn.Module):

    def __init__(self, input_size, output_size, num_emojis, dropout):
        super().__init__()
        self.V = torch.nn.Parameter(torch.empty(num_emojis, output_size).
            uniform_(-0.1, 0.1))
        self.dropout = torch.nn.Dropout(p=dropout)
        if not input_size == output_size:
            self.is_proj = True
            self.W = torch.nn.Parameter(torch.empty(input_size, output_size
                ).uniform_(-0.1, 0.1))
            self.tanh = torch.nn.Tanh()
        else:
            self.is_proj = False

    def project_embeddings(self, embeddings_array):
        return self.tanh(torch.mm(torch.Tensor(embeddings_array), self.W)
            ).detach().numpy()

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