# 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_7/inductor_cache/k2/ck2ifjln7mgxcq7f4l4ctfrzcqh5srlzt3rsj2xkjsdz5mju6rli.py
# Topologically Sorted Source Nodes: [square, square_sum, add, sqrt, truediv, square_1, x_norm, x_2, sign, abs_1, x_3, x_4, mul], Original ATen: [aten.pow, aten.sum, aten.add, aten.sqrt, aten.div, aten.mul, aten.sign, aten.abs]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   add => add
#   mul => mul_1
#   sign => sign
#   sqrt => sqrt
#   square => pow_1
#   square_1 => pow_2
#   square_sum => sum_1
#   truediv => div
#   x_2 => mul, sum_3
#   x_3 => add_4
#   x_4 => pow_6
#   x_norm => add_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1, 4], True), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-12), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_2, 100), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div, 2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, %pow_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%permute, [4], True), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_3, %permute_1), kwargs = {})
#   %sign : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%view_1,), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%view_1,), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, 1e-12), kwargs = {})
#   %pow_6 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%add_4, %view_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sign, %pow_6), kwargs = {})
triton_poi_fused_abs_add_div_mul_pow_sign_sqrt_sum_0 = async_compile.triton('triton_poi_fused_abs_add_div_mul_pow_sign_sqrt_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: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_add_div_mul_pow_sign_sqrt_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, '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_abs_add_div_mul_pow_sign_sqrt_sum_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp14 = tl.load(in_ptr1 + (0))
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp27 = tl.load(in_ptr2 + (0))
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
    tmp44 = tl.load(in_ptr3 + (0))
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = 1e-12
    tmp12 = tmp10 + tmp11
    tmp13 = libdevice.sqrt(tmp12)
    tmp16 = 0.01
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp13 + tmp18
    tmp20 = tmp0 / tmp19
    tmp21 = tmp2 / tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp5 / tmp19
    tmp24 = tmp22 + tmp23
    tmp25 = tmp8 / tmp19
    tmp26 = tmp24 + tmp25
    tmp29 = tmp28 * tmp28
    tmp30 = tmp29 + tmp11
    tmp31 = libdevice.sqrt(tmp30)
    tmp32 = tmp31 + tmp18
    tmp33 = tmp28 / tmp32
    tmp34 = tmp26 * tmp33
    tmp35 = tl.full([1], 0, tl.int32)
    tmp36 = tmp35 < tmp34
    tmp37 = tmp36.to(tl.int8)
    tmp38 = tmp34 < tmp35
    tmp39 = tmp38.to(tl.int8)
    tmp40 = tmp37 - tmp39
    tmp41 = tmp40.to(tmp34.dtype)
    tmp42 = tl_math.abs(tmp34)
    tmp43 = tmp42 + tmp11
    tmp46 = 0.1
    tmp47 = tmp45 * tmp46
    tmp48 = tmp47 * tmp47
    tmp49 = libdevice.pow(tmp43, tmp48)
    tmp50 = tmp41 * tmp49
    tl.store(in_out_ptr0 + (x2), tmp50, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, ), (1, ))
    assert_size_stride(primals_3, (1, 1, 1), (1, 1, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4, 1, 1), (16, 64, 4, 1, 64, 64), 0); del buf0  # reuse
        buf2 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 16, 4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [square, square_sum, add, sqrt, truediv, square_1, x_norm, x_2, sign, abs_1, x_3, x_4, mul], Original ATen: [aten.pow, aten.sum, aten.add, aten.sqrt, aten.div, aten.mul, aten.sign, aten.abs]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_add_div_mul_pow_sign_sqrt_sum_0.run(buf2, primals_1, primals_2, primals_3, primals_4, 64, grid=grid(64), stream=stream0)
    return (buf2, primals_1, primals_2, primals_3, primals_4, )


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((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, 1, 1), (1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
import torch.nn.functional as F


def unfold2d(x, kernel_size: 'int', stride: 'int', padding: 'int'):
    x = F.pad(x, [padding] * 4)
    bs, in_c, h, w = x.size()
    ks = kernel_size
    strided_x = x.as_strided((bs, in_c, (h - ks) // stride + 1, (w - ks) //
        stride + 1, ks, ks), (in_c * h * w, h * w, stride * w, stride, w, 1))
    return strided_x


class SharpenedCosineSimilarity(nn.Module):

    def __init__(self, in_channels=1, out_channels=1, kernel_size=1, stride
        =1, padding=0, eps=1e-12):
        super(SharpenedCosineSimilarity, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.eps = eps
        self.padding = int(padding)
        w = torch.empty(out_channels, in_channels, kernel_size, kernel_size)
        nn.init.xavier_uniform_(w)
        self.w = nn.Parameter(w.view(out_channels, in_channels, -1),
            requires_grad=True)
        self.p_scale = 10
        p_init = 2 ** 0.5 * self.p_scale
        self.register_parameter('p', nn.Parameter(torch.empty(out_channels)))
        nn.init.constant_(self.p, p_init)
        self.q_scale = 100
        self.register_parameter('q', nn.Parameter(torch.empty(1)))
        nn.init.constant_(self.q, 10)

    def forward(self, x):
        x = unfold2d(x, kernel_size=self.kernel_size, stride=self.stride,
            padding=self.padding)
        n, c, h, w, _, _ = x.shape
        x = x.reshape(n, c, h, w, -1)
        square_sum = torch.sum(torch.square(x), [1, 4], keepdim=True)
        x_norm = torch.add(torch.sqrt(square_sum + self.eps), torch.square(
            self.q / self.q_scale))
        square_sum = torch.sum(torch.square(self.w), [1, 2], keepdim=True)
        w_norm = torch.add(torch.sqrt(square_sum + self.eps), torch.square(
            self.q / self.q_scale))
        x = torch.einsum('nchwl,vcl->nvhw', x / x_norm, self.w / w_norm)
        sign = torch.sign(x)
        x = torch.abs(x) + self.eps
        x = x.pow(torch.square(self.p / self.p_scale).view(1, -1, 1, 1))
        return sign * x


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


def get_init_inputs():
    return [[], {}]

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 libdevice, math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_abs_add_div_mul_pow_sign_sqrt_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr1 + 0)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp27 = tl.load(in_ptr2 + 0)
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK])
    tmp44 = tl.load(in_ptr3 + 0)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = 1e-12
    tmp12 = tmp10 + tmp11
    tmp13 = libdevice.sqrt(tmp12)
    tmp16 = 0.01
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp13 + tmp18
    tmp20 = tmp0 / tmp19
    tmp21 = tmp2 / tmp19
    tmp22 = tmp20 + tmp21
    tmp23 = tmp5 / tmp19
    tmp24 = tmp22 + tmp23
    tmp25 = tmp8 / tmp19
    tmp26 = tmp24 + tmp25
    tmp29 = tmp28 * tmp28
    tmp30 = tmp29 + tmp11
    tmp31 = libdevice.sqrt(tmp30)
    tmp32 = tmp31 + tmp18
    tmp33 = tmp28 / tmp32
    tmp34 = tmp26 * tmp33
    tmp35 = tl.full([1], 0, tl.int32)
    tmp36 = tmp35 < tmp34
    tmp37 = tmp36.to(tl.int8)
    tmp38 = tmp34 < tmp35
    tmp39 = tmp38.to(tl.int8)
    tmp40 = tmp37 - tmp39
    tmp41 = tmp40.to(tmp34.dtype)
    tmp42 = tl_math.abs(tmp34)
    tmp43 = tmp42 + tmp11
    tmp46 = 0.1
    tmp47 = tmp45 * tmp46
    tmp48 = tmp47 * tmp47
    tmp49 = libdevice.pow(tmp43, tmp48)
    tmp50 = tmp41 * tmp49
    tl.store(in_out_ptr0 + x2, tmp50, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (1, 1, 1), (1, 1, 1))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4, 1), (16, 64, 4, 1, 64),
            torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4, 1, 1), (16, 64, 4, 1, 
            64, 64), 0)
        del buf0
        buf2 = reinterpret_tensor(buf1, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused_abs_add_div_mul_pow_sign_sqrt_sum_0[grid(64)](buf2,
            primals_1, primals_2, primals_3, primals_4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf2, primals_1, primals_2, primals_3, primals_4


def unfold2d(x, kernel_size: 'int', stride: 'int', padding: 'int'):
    x = F.pad(x, [padding] * 4)
    bs, in_c, h, w = x.size()
    ks = kernel_size
    strided_x = x.as_strided((bs, in_c, (h - ks) // stride + 1, (w - ks) //
        stride + 1, ks, ks), (in_c * h * w, h * w, stride * w, stride, w, 1))
    return strided_x


class SharpenedCosineSimilarityNew(nn.Module):

    def __init__(self, in_channels=1, out_channels=1, kernel_size=1, stride
        =1, padding=0, eps=1e-12):
        super(SharpenedCosineSimilarityNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.eps = eps
        self.padding = int(padding)
        w = torch.empty(out_channels, in_channels, kernel_size, kernel_size)
        nn.init.xavier_uniform_(w)
        self.w = nn.Parameter(w.view(out_channels, in_channels, -1),
            requires_grad=True)
        self.p_scale = 10
        p_init = 2 ** 0.5 * self.p_scale
        self.register_parameter('p', nn.Parameter(torch.empty(out_channels)))
        nn.init.constant_(self.p, p_init)
        self.q_scale = 100
        self.register_parameter('q', nn.Parameter(torch.empty(1)))
        nn.init.constant_(self.q, 10)

    def forward(self, input_0):
        primals_3 = self.w
        primals_2 = self.p
        primals_4 = self.q
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
