# 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_0/inductor_cache/2b/c2bm5664wi7zvbke7gwfeiakuapvcq7cpije3apjmoe2ggjtvbju.py
# Topologically Sorted Source Nodes: [mul, truediv, cos, add, cutoffs, lt, float_1, cutoffs_1, neg, neg_1, add_1, mul_3, exp, sub, pow_1, mul_4, exp_1, mul_5], Original ATen: [aten.mul, aten.div, aten.cos, aten.add, aten.lt, aten._to_copy, aten.neg, aten.exp, aten.sub, aten.pow]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   cos => cos
#   cutoffs => mul_1
#   cutoffs_1 => mul_2
#   exp => exp
#   exp_1 => exp_1
#   float_1 => convert_element_type
#   lt => lt
#   mul => mul
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   neg => neg
#   neg_1 => neg_1
#   pow_1 => pow_1
#   sub => sub
#   truediv => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, 3.141592653589793), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, 5.0), kwargs = {})
#   %cos : [num_users=1] = call_function[target=torch.ops.aten.cos.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cos, 1.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%unsqueeze, 5.0), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%lt, torch.float32), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %convert_element_type), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%primals_2,), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%unsqueeze,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%neg_1, 0.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 1.0), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_3,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%exp, %primals_3), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %pow_1), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_4,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %exp_1), kwargs = {})
triton_poi_fused__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_0 = async_compile.triton('triton_poi_fused__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_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=[16384], 
    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__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 50)
    x0 = xindex % 50
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 3.141592653589793
    tmp2 = tmp0 * tmp1
    tmp3 = 0.2
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.cos(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = 5.0
    tmp11 = tmp0 < tmp10
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp9 * tmp12
    tmp15 = -tmp14
    tmp16 = -tmp0
    tmp17 = 0.0
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 * tmp6
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp15 * tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp13 * tmp25
    tl.store(out_ptr0 + (x2), tmp26, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (50, ), (1, ))
    assert_size_stride(primals_3, (50, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 50), (3200, 800, 200, 50, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, truediv, cos, add, cutoffs, lt, float_1, cutoffs_1, neg, neg_1, add_1, mul_3, exp, sub, pow_1, mul_4, exp_1, mul_5], Original ATen: [aten.mul, aten.div, aten.cos, aten.add, aten.lt, aten._to_copy, aten.neg, aten.exp, aten.sub, aten.pow]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_0.run(primals_1, primals_2, primals_3, buf0, 12800, grid=grid(12800), stream=stream0)
    return (buf0, 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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((50, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((50, ), (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
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 math as tl_math
import math
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__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 50
    x0 = xindex % 50
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 3.141592653589793
    tmp2 = tmp0 * tmp1
    tmp3 = 0.2
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.cos(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = 5.0
    tmp11 = tmp0 < tmp10
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp9 * tmp12
    tmp15 = -tmp14
    tmp16 = -tmp0
    tmp17 = 0.0
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18 * tmp6
    tmp20 = tl_math.exp(tmp19)
    tmp22 = tmp20 - tmp21
    tmp23 = tmp22 * tmp22
    tmp24 = tmp15 * tmp23
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp13 * tmp25
    tl.store(out_ptr0 + x2, tmp26, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (50,), (1,))
    assert_size_stride(primals_3, (50,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 50), (3200, 800, 200, 50, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_cos_div_exp_lt_mul_neg_pow_sub_0[grid
            (12800)](primals_1, primals_2, primals_3, buf0, 12800, XBLOCK=
            256, num_warps=4, num_stages=1)
    return buf0, primals_1, primals_2, primals_3


class CosineCutoff(nn.Module):

    def __init__(self, cutoff_lower=0.0, cutoff_upper=5.0):
        super(CosineCutoff, self).__init__()
        self.cutoff_lower = cutoff_lower
        self.cutoff_upper = cutoff_upper

    def forward(self, distances):
        if self.cutoff_lower > 0:
            cutoffs = 0.5 * (torch.cos(math.pi * (2 * (distances - self.
                cutoff_lower) / (self.cutoff_upper - self.cutoff_lower) + 
                1.0)) + 1.0)
            cutoffs = cutoffs * (distances < self.cutoff_upper).float()
            cutoffs = cutoffs * (distances > self.cutoff_lower).float()
            return cutoffs
        else:
            cutoffs = 0.5 * (torch.cos(distances * math.pi / self.
                cutoff_upper) + 1.0)
            cutoffs = cutoffs * (distances < self.cutoff_upper).float()
            return cutoffs


class ExpNormalSmearingNew(nn.Module):

    def __init__(self, cutoff_lower=0.0, cutoff_upper=5.0, num_rbf=50,
        trainable=True):
        super(ExpNormalSmearingNew, self).__init__()
        self.cutoff_lower = cutoff_lower
        self.cutoff_upper = cutoff_upper
        self.num_rbf = num_rbf
        self.trainable = trainable
        self.cutoff_fn = CosineCutoff(0, cutoff_upper)
        self.alpha = 5.0 / (cutoff_upper - cutoff_lower)
        means, betas = self._initial_params()
        if trainable:
            self.register_parameter('means', nn.Parameter(means))
            self.register_parameter('betas', nn.Parameter(betas))
        else:
            self.register_buffer('means', means)
            self.register_buffer('betas', betas)

    def _initial_params(self):
        start_value = torch.exp(torch.scalar_tensor(-self.cutoff_upper +
            self.cutoff_lower))
        means = torch.linspace(start_value, 1, self.num_rbf)
        betas = torch.tensor([(2 / self.num_rbf * (1 - start_value)) ** -2] *
            self.num_rbf)
        return means, betas

    def reset_parameters(self):
        means, betas = self._initial_params()
        self.means.data.copy_(means)
        self.betas.data.copy_(betas)

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