# 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/r6/cr6rugw7bu6dmxtlb5kvfk5hqvgylasexbfbjopqctk2ib7huawo.py
# Topologically Sorted Source Nodes: [truediv_1, pow_1, mul, add, pow_2, mul_1, add_1, pow_3, mul_2, env_val, lt, zeros_like, d_cutoff, mul_3, sin, mul_4], Original ATen: [aten.reciprocal, aten.mul, aten.pow, aten.add, aten.lt, aten.zeros_like, aten.where, aten.sin]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   d_cutoff => where
#   env_val => add_2
#   lt => lt
#   mul => mul_1
#   mul_1 => mul_2
#   mul_2 => mul_3
#   mul_3 => mul_4
#   mul_4 => mul_5
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   sin => sin
#   truediv_1 => mul, reciprocal
#   zeros_like => full_default
# Graph fragment:
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%unsqueeze,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%unsqueeze, 5), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, -28.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%unsqueeze, 6), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, 48), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mul_2), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%unsqueeze, 7), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_3, -21.0), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_3), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%unsqueeze, 1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4, 1], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %add_2, %full_default), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %unsqueeze), kwargs = {})
#   %sin : [num_users=1] = call_function[target=torch.ops.aten.sin.default](args = (%mul_4,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %sin), kwargs = {})
triton_poi_fused_add_lt_mul_pow_reciprocal_sin_where_zeros_like_0 = async_compile.triton('triton_poi_fused_add_lt_mul_pow_reciprocal_sin_where_zeros_like_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=[1024], 
    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_lt_mul_pow_reciprocal_sin_where_zeros_like_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_lt_mul_pow_reciprocal_sin_where_zeros_like_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 < tmp3
    tmp5 = tl.full([1], 1, tl.int32)
    tmp6 = tmp5 / tmp2
    tmp7 = tmp6 * tmp3
    tmp8 = tmp2 * tmp2
    tmp9 = tmp8 * tmp8
    tmp10 = tmp9 * tmp2
    tmp11 = -28.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = tmp8 * tmp2
    tmp15 = tmp14 * tmp14
    tmp16 = 48.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp15 * tmp2
    tmp20 = -21.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = 0.0
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp26 = tmp25 * tmp2
    tmp27 = tl_math.sin(tmp26)
    tmp28 = tmp24 * tmp27
    tl.store(out_ptr0 + (x2), tmp28, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [truediv_1, pow_1, mul, add, pow_2, mul_1, add_1, pow_3, mul_2, env_val, lt, zeros_like, d_cutoff, mul_3, sin, mul_4], Original ATen: [aten.reciprocal, aten.mul, aten.pow, aten.add, aten.lt, aten.zeros_like, aten.where, aten.sin]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_lt_mul_pow_reciprocal_sin_where_zeros_like_0.run(primals_1, primals_2, buf0, 1024, grid=grid(1024), stream=stream0)
    return (buf0, primals_1, primals_2, )


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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 numpy as np
import torch.nn as nn


class Envelope(nn.Module):

    def __init__(self, exponent):
        super(Envelope, self).__init__()
        self.exponent = exponent
        self.p = exponent + 1
        self.a = -(self.p + 1) * (self.p + 2) / 2
        self.b = self.p * (self.p + 2)
        self.c = -self.p * (self.p + 1) / 2

    def forward(self, inputs):
        env_val = 1 / inputs + self.a * inputs ** (self.p - 1
            ) + self.b * inputs ** self.p + self.c * inputs ** (self.p + 1)
        return torch.where(inputs < 1, env_val, torch.zeros_like(inputs))


class BesselBasisLayer(nn.Module):

    def __init__(self, num_radial, cutoff, envelope_exponent=5):
        super(BesselBasisLayer, self).__init__()
        self.num_radial = num_radial
        self.cutoff = cutoff
        self.envelope = Envelope(envelope_exponent)
        freq_init = np.pi * torch.arange(1, num_radial + 1)
        self.frequencies = nn.Parameter(freq_init)

    def forward(self, inputs):
        d_scaled = inputs / self.cutoff
        d_scaled = torch.unsqueeze(d_scaled, -1)
        d_cutoff = self.envelope(d_scaled)
        return d_cutoff * torch.sin(self.frequencies * d_scaled)


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


def get_init_inputs():
    return [[], {'num_radial': 4, 'cutoff': 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._inductor.runtime.triton_helpers import math as tl_math
import numpy as np
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


@triton.jit
def triton_poi_fused_add_lt_mul_pow_reciprocal_sin_where_zeros_like_0(in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 < tmp3
    tmp5 = tl.full([1], 1, tl.int32)
    tmp6 = tmp5 / tmp2
    tmp7 = tmp6 * tmp3
    tmp8 = tmp2 * tmp2
    tmp9 = tmp8 * tmp8
    tmp10 = tmp9 * tmp2
    tmp11 = -28.0
    tmp12 = tmp10 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = tmp8 * tmp2
    tmp15 = tmp14 * tmp14
    tmp16 = 48.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp15 * tmp2
    tmp20 = -21.0
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = 0.0
    tmp24 = tl.where(tmp4, tmp22, tmp23)
    tmp26 = tmp25 * tmp2
    tmp27 = tl_math.sin(tmp26)
    tmp28 = tmp24 * tmp27
    tl.store(out_ptr0 + x2, tmp28, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_lt_mul_pow_reciprocal_sin_where_zeros_like_0[grid
            (1024)](primals_1, primals_2, buf0, 1024, XBLOCK=128, num_warps
            =4, num_stages=1)
    return buf0, primals_1, primals_2


class Envelope(nn.Module):

    def __init__(self, exponent):
        super(Envelope, self).__init__()
        self.exponent = exponent
        self.p = exponent + 1
        self.a = -(self.p + 1) * (self.p + 2) / 2
        self.b = self.p * (self.p + 2)
        self.c = -self.p * (self.p + 1) / 2

    def forward(self, inputs):
        env_val = 1 / inputs + self.a * inputs ** (self.p - 1
            ) + self.b * inputs ** self.p + self.c * inputs ** (self.p + 1)
        return torch.where(inputs < 1, env_val, torch.zeros_like(inputs))


class BesselBasisLayerNew(nn.Module):

    def __init__(self, num_radial, cutoff, envelope_exponent=5):
        super(BesselBasisLayerNew, self).__init__()
        self.num_radial = num_radial
        self.cutoff = cutoff
        self.envelope = Envelope(envelope_exponent)
        freq_init = np.pi * torch.arange(1, num_radial + 1)
        self.frequencies = nn.Parameter(freq_init)

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