# 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/mb/cmbfb43iymo2jb2srmgr7hae3y37bqflfj3wrihlrjlmhb33mlpk.py
# Topologically Sorted Source Nodes: [out, out_1, ones_like, norm], Original ATen: [aten.pow, aten.sum, aten.ones_like, aten.convolution]
# Source node to ATen node mapping:
#   norm => convolution
#   ones_like => full_default
#   out => pow_1
#   out_1 => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1, 1, 4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%sum_1, %full_default, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_ones_like_pow_sum_0 = async_compile.triton('triton_poi_fused_convolution_ones_like_pow_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_ones_like_pow_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_convolution_ones_like_pow_sum_0(in_ptr0, out_ptr0, 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)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tl.store(out_ptr0 + (x2), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/j7/cj73aqsbb35sktpndjl2h4bp2q3r2mtzl2ahq3tg56cbgykvpzdx.py
# Topologically Sorted Source Nodes: [out, out_1, ones_like, norm], Original ATen: [aten.pow, aten.sum, aten.ones_like, aten.convolution]
# Source node to ATen node mapping:
#   norm => convolution
#   ones_like => full_default
#   out => pow_1
#   out_1 => sum_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1], True), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([1, 1, 4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%sum_1, %full_default, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_ones_like_pow_sum_1 = async_compile.triton('triton_poi_fused_convolution_ones_like_pow_sum_1', '''
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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_ones_like_pow_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_convolution_ones_like_pow_sum_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2i/c2ioqypkkpd7imw74zurye5it7p2mkwxd6hrhzx2apuyslwclmld.py
# Topologically Sorted Source Nodes: [square_1, sum_2, sqrt, weight], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.div]
# Source node to ATen node mapping:
#   sqrt => sqrt
#   square_1 => pow_2
#   sum_2 => sum_2
#   weight => div_1
# Graph fragment:
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_3, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [1, 2, 3], True), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_2,), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_3, %sqrt), kwargs = {})
triton_per_fused_div_pow_sqrt_sum_2 = async_compile.triton('triton_per_fused_div_pow_sqrt_sum_2', '''
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.persistent_reduction(
    size_hints=[4, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_pow_sqrt_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_pow_sqrt_sum_2(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (64*x0)), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp0 / tmp6
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp6, xmask)
    tl.store(out_ptr0 + (r1 + (64*x0)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/bh/cbhevdbkvcnrnkcsrexk6bxenen7fkefkpkqbhd3c4ghkofpljxi.py
# Topologically Sorted Source Nodes: [neg, truediv, q, add, norm_1, sqrt_1, out_2, abs_1, magnitude, sign, out_3, out_4], Original ATen: [aten.neg, aten.div, aten.exp, aten.add, aten.sqrt, aten.abs, aten.sign, aten.pow, aten.mul]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   add => add
#   magnitude => add_2
#   neg => neg
#   norm_1 => add_1
#   out_2 => div_2
#   out_3 => pow_3
#   out_4 => mul
#   q => exp
#   sign => sign
#   sqrt_1 => sqrt_1
#   truediv => div
# Graph fragment:
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%primals_2,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, 0.3), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, 1e-06), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %add), kwargs = {})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {})
#   %div_2 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%convolution_1, %sqrt_1), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%div_2,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, 1e-06), kwargs = {})
#   %sign : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%div_2,), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%add_2, %view), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_3, %sign), kwargs = {})
triton_poi_fused_abs_add_div_exp_mul_neg_pow_sign_sqrt_3 = async_compile.triton('triton_poi_fused_abs_add_div_exp_mul_neg_pow_sign_sqrt_3', '''
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: '*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_exp_mul_neg_pow_sign_sqrt_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_exp_mul_neg_pow_sign_sqrt_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 9
    x2 = (xindex // 36)
    x1 = (xindex // 9) % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + (9*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (0))
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp15 = tl.load(in_ptr3 + (x1), xmask, eviction_policy='evict_last')
    tmp4 = -tmp3
    tmp5 = 3.3333333333333335
    tmp6 = tmp4 * tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = 1e-06
    tmp9 = tmp7 + tmp8
    tmp10 = tmp1 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = tmp0 / tmp11
    tmp13 = tl_math.abs(tmp12)
    tmp14 = tmp13 + tmp8
    tmp16 = 0.2
    tmp17 = tmp15 * tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = libdevice.pow(tmp14, tmp18)
    tmp20 = tl.full([1], 0, tl.int32)
    tmp21 = tmp20 < tmp12
    tmp22 = tmp21.to(tl.int8)
    tmp23 = tmp12 < tmp20
    tmp24 = tmp23.to(tl.int8)
    tmp25 = tmp22 - tmp24
    tmp26 = tmp25.to(tmp12.dtype)
    tmp27 = tmp19 * tmp26
    tl.store(out_ptr0 + (x3), tmp27, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1, ones_like, norm], Original ATen: [aten.pow, aten.sum, aten.ones_like, aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_ones_like_pow_sum_0.run(primals_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((1, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1, ones_like, norm], Original ATen: [aten.pow, aten.sum, aten.ones_like, aten.convolution]
        triton_poi_fused_convolution_ones_like_pow_sum_1.run(buf1, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [out, out_1, ones_like, norm], Original ATen: [aten.pow, aten.sum, aten.ones_like, aten.convolution]
        buf2 = extern_kernels.convolution(buf0, buf1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 3, 3), (9, 9, 3, 1))
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf4 = reinterpret_tensor(buf3, (4, 1, 1, 1), (1, 1, 1, 1), 0); del buf3  # reuse
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [square_1, sum_2, sqrt, weight], Original ATen: [aten.pow, aten.sum, aten.sqrt, aten.div]
        triton_per_fused_div_pow_sqrt_sum_2.run(buf4, primals_3, buf5, 4, 64, grid=grid(4), stream=stream0)
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(primals_1, buf5, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 3, 3), (36, 9, 3, 1))
        buf7 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [neg, truediv, q, add, norm_1, sqrt_1, out_2, abs_1, magnitude, sign, out_3, out_4], Original ATen: [aten.neg, aten.div, aten.exp, aten.add, aten.sqrt, aten.abs, aten.sign, aten.pow, aten.mul]
        triton_poi_fused_abs_add_div_exp_mul_neg_pow_sign_sqrt_3.run(buf6, buf2, primals_2, primals_4, buf7, 144, grid=grid(144), stream=stream0)
    return (buf7, primals_1, primals_2, primals_3, primals_4, buf2, buf4, buf5, buf6, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (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


class SharpenedCosineSimilarity(nn.Conv2d):

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size,
        stride=1, padding=None, dilation=1, groups: 'int'=1, bias: 'bool'=
        False, q_init: 'float'=10, p_init: 'float'=1.0, q_scale: 'float'=
        0.3, p_scale: 'float'=5, eps: 'float'=1e-06):
        if padding is None:
            if int(torch.__version__.split('.')[1]) >= 10:
                padding = 'same'
            else:
                padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
        if isinstance(kernel_size, int):
            kernel_size = kernel_size, kernel_size
        bias = False
        assert dilation == 1, 'Dilation has to be 1 to use AvgPool2d as L2-Norm backend.'
        assert groups == in_channels or groups == 1, 'Either depthwise or full convolution. Grouped not supported'
        super(SharpenedCosineSimilarity, self).__init__(in_channels,
            out_channels, kernel_size, stride, padding, dilation, groups, bias)
        self.p_scale = p_scale
        self.q_scale = q_scale
        self.p = torch.nn.Parameter(torch.full((out_channels,), float(
            p_init * self.p_scale)))
        self.q = torch.nn.Parameter(torch.full((1,), float(q_init * self.
            q_scale)))
        self.eps = eps

    def forward(self, inp: 'torch.Tensor') ->torch.Tensor:
        out = inp.square()
        if self.groups == 1:
            out = out.sum(1, keepdim=True)
        q = torch.exp(-self.q / self.q_scale)
        norm = F.conv2d(out, torch.ones_like(self.weight[:1, :1]), None,
            self.stride, self.padding, self.dilation)
        norm = norm + (q + self.eps)
        weight = self.weight / self.weight.square().sum(dim=(1, 2, 3),
            keepdim=True).sqrt()
        out = F.conv2d(inp, weight, self.bias, self.stride, self.padding,
            self.dilation, self.groups) / norm.sqrt()
        magnitude = out.abs() + self.eps
        sign = out.sign()
        p = torch.exp(self.p / self.p_scale)
        out = magnitude.pow(p.view(1, -1, 1, 1))
        out = out * sign
        return out


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'kernel_size': 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, math as tl_math
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_convolution_ones_like_pow_sum_0(in_ptr0, out_ptr0,
    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)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_convolution_ones_like_pow_sum_1(out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 1.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_per_fused_div_pow_sqrt_sum_2(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = libdevice.sqrt(tmp5)
    tmp7 = tmp0 / tmp6
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, xmask)
    tl.store(out_ptr0 + (r1 + 64 * x0), tmp7, xmask)


@triton.jit
def triton_poi_fused_abs_add_div_exp_mul_neg_pow_sign_sqrt_3(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 9
    x2 = xindex // 36
    x1 = xindex // 9 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 9 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp15 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp4 = -tmp3
    tmp5 = 3.3333333333333335
    tmp6 = tmp4 * tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp8 = 1e-06
    tmp9 = tmp7 + tmp8
    tmp10 = tmp1 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = tmp0 / tmp11
    tmp13 = tl_math.abs(tmp12)
    tmp14 = tmp13 + tmp8
    tmp16 = 0.2
    tmp17 = tmp15 * tmp16
    tmp18 = tl_math.exp(tmp17)
    tmp19 = libdevice.pow(tmp14, tmp18)
    tmp20 = tl.full([1], 0, tl.int32)
    tmp21 = tmp20 < tmp12
    tmp22 = tmp21.to(tl.int8)
    tmp23 = tmp12 < tmp20
    tmp24 = tmp23.to(tl.int8)
    tmp25 = tmp22 - tmp24
    tmp26 = tmp25.to(tmp12.dtype)
    tmp27 = tmp19 * tmp26
    tl.store(out_ptr0 + x3, tmp27, 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, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_ones_like_pow_sum_0[grid(64)](primals_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((1, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        triton_poi_fused_convolution_ones_like_pow_sum_1[grid(16)](buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = extern_kernels.convolution(buf0, buf1, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 3, 3), (9, 9, 3, 1))
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 1, 1, 1), (1, 4, 4, 4), torch.float32)
        buf4 = reinterpret_tensor(buf3, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf3
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_div_pow_sqrt_sum_2[grid(4)](buf4, primals_3, buf5,
            4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf6 = extern_kernels.convolution(primals_1, buf5, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 3, 3), (36, 9, 3, 1))
        buf7 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_poi_fused_abs_add_div_exp_mul_neg_pow_sign_sqrt_3[grid(144)](
            buf6, buf2, primals_2, primals_4, buf7, 144, XBLOCK=128,
            num_warps=4, num_stages=1)
    return (buf7, primals_1, primals_2, primals_3, primals_4, buf2, buf4,
        buf5, buf6)


class SharpenedCosineSimilarityNew(nn.Conv2d):

    def __init__(self, in_channels: 'int', out_channels: 'int', kernel_size,
        stride=1, padding=None, dilation=1, groups: 'int'=1, bias: 'bool'=
        False, q_init: 'float'=10, p_init: 'float'=1.0, q_scale: 'float'=
        0.3, p_scale: 'float'=5, eps: 'float'=1e-06):
        if padding is None:
            if int(torch.__version__.split('.')[1]) >= 10:
                padding = 'same'
            else:
                padding = (stride - 1 + dilation * (kernel_size - 1)) // 2
        if isinstance(kernel_size, int):
            kernel_size = kernel_size, kernel_size
        bias = False
        assert dilation == 1, 'Dilation has to be 1 to use AvgPool2d as L2-Norm backend.'
        assert groups == in_channels or groups == 1, 'Either depthwise or full convolution. Grouped not supported'
        super(SharpenedCosineSimilarityNew, self).__init__(in_channels,
            out_channels, kernel_size, stride, padding, dilation, groups, bias)
        self.p_scale = p_scale
        self.q_scale = q_scale
        self.p = torch.nn.Parameter(torch.full((out_channels,), float(
            p_init * self.p_scale)))
        self.q = torch.nn.Parameter(torch.full((1,), float(q_init * self.
            q_scale)))
        self.eps = eps

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