# 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_8/inductor_cache/p4/cp4l5pdv4j5bky7t2prpvw2onlthn6s2qqt4dxzvxmarl4tomzgu.py
# Topologically Sorted Source Nodes: [ang_features, ang_sumFeatures], Original ATen: [aten.tanh, aten.add]
# Source node to ATen node mapping:
#   ang_features => tanh
#   ang_sumFeatures => add
# Graph fragment:
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%view_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%tanh, %primals_4), kwargs = {})
triton_poi_fused_add_tanh_0 = async_compile.triton('triton_poi_fused_add_tanh_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: '*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_tanh_0', '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_add_tanh_0(in_ptr0, in_ptr1, 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)
    tmp2 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = tmp1 + tmp2
    tl.store(out_ptr0 + (x0), tmp3, 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 = 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))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (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: [out_dense1], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ang_features, ang_sumFeatures], Original ATen: [aten.tanh, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_tanh_0.run(buf0, primals_4, buf1, 256, grid=grid(256), stream=stream0)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [angs], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_6
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), primals_5, )


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.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = 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])
    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 torch.nn as nn


class CAE(nn.Module):
    """
    The Cobb Angle Estimator (CAE), which :
        1. maps #nDense1 landmark features to #nDense2 angle features
        2. adds the #nDense2 angle features (from step 1) to  #nDense2 landmarks features (from previous layer)
        3. maps summed #nDense2 angle features (from step 2) to #nDense3 angles estimations
    (Paper: Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net)
    """

    def __init__(self, nDense1, nDense2, nDense3):
        """
        Initialize the building blocks of CAE.
        :param nDense1: #features from the previous SLE (#features is arbitrary) (see 'SLE' class for details)
        :param nDense2: (#features = 2 * #landmarks)
        :param nDense3: (#features = #angles)
        """
        super(CAE, self).__init__()
        self.dense1 = nn.Linear(nDense1, nDense2)
        self.tanh = nn.Tanh()
        self.dense2 = nn.Linear(nDense2, nDense3)

    def forward(self, lm_features, lm_coordinates):
        """
        The forwarding pass of CAE, which make cobb angles estimations from two landmark features.
        :param lm_features: the output 'y1' of SLE (see 'SLE' class for details)
        :param lm_coordinates: the output 'y2' of SLE (see 'SLE' class for details)
        :return angs: #nDense3 angle estimations
        """
        out_dense1 = self.dense1(lm_features)
        ang_features = self.tanh(out_dense1)
        ang_sumFeatures = ang_features + lm_coordinates
        angs = self.dense2(ang_sumFeatures)
        return angs


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


def get_init_inputs():
    return [[], {'nDense1': 4, 'nDense2': 4, 'nDense3': 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
import torch.nn as nn
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_add_tanh_0(in_ptr0, in_ptr1, 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)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = tmp1 + tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = 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))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (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_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](buf0, primals_4, buf1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_6
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), primals_5


class CAENew(nn.Module):
    """
    The Cobb Angle Estimator (CAE), which :
        1. maps #nDense1 landmark features to #nDense2 angle features
        2. adds the #nDense2 angle features (from step 1) to  #nDense2 landmarks features (from previous layer)
        3. maps summed #nDense2 angle features (from step 2) to #nDense3 angles estimations
    (Paper: Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net)
    """

    def __init__(self, nDense1, nDense2, nDense3):
        """
        Initialize the building blocks of CAE.
        :param nDense1: #features from the previous SLE (#features is arbitrary) (see 'SLE' class for details)
        :param nDense2: (#features = 2 * #landmarks)
        :param nDense3: (#features = #angles)
        """
        super(CAENew, self).__init__()
        self.dense1 = nn.Linear(nDense1, nDense2)
        self.tanh = nn.Tanh()
        self.dense2 = nn.Linear(nDense2, nDense3)

    def forward(self, input_0, input_1):
        primals_1 = self.dense1.weight
        primals_2 = self.dense1.bias
        primals_5 = self.dense2.weight
        primals_6 = self.dense2.bias
        primals_3 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
