# AOT ID: ['0_inference']
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_1/inductor_cache/ap/capaaxd63aalh4fqdcmjippyj3avhxm5vrcqzwydwiwppycshonw.py
# Topologically Sorted Source Nodes: [sub, pow_1, sum_1, neg], Original ATen: [aten.sub, aten.pow, aten.sum, aten.neg]
# Source node to ATen node mapping:
#   neg => neg
#   pow_1 => pow_1
#   sub => sub
#   sum_1 => sum_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
triton_poi_fused_neg_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_neg_pow_sub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_neg_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_neg_pow_sub_sum_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
    x3 = (xindex // 4)
    x0 = xindex % 4
    x2 = (xindex // 16)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = -tmp18
    tl.store(out_ptr0 + (x4), tmp19, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, sum_1, neg], Original ATen: [aten.sub, aten.pow, aten.sum, aten.neg]
        stream0 = get_raw_stream(0)
        triton_poi_fused_neg_pow_sub_sum_0.run(arg0_1, arg1_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    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
from torch import nn
from abc import abstractmethod
from typing import Union
import torch.utils.data


class Similarity(nn.Module):
    """Base class for similarity functions."""

    @abstractmethod
    def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Compute pair-wise similarities.

        :param x: shape: (*, n, d)
            The first vectors.
        :param y: shape: (*, m, d)
            The second vectors.

        :return: shape: (*, n, m)
            The similarity values.
        """
        raise NotImplementedError

    def one_to_one(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Compute batched one-to-one similarities.

        :param x: shape: (*, d)
            The first vectors.
        :param y: shape: (*, d)
            The second vectors.

        :return: shape: (*)
            The similarity values.
        """
        return self(x.unsqueeze(dim=-2), y.unsqueeze(dim=-2)).squeeze(dim=-1
            ).squeeze(dim=-1)


class NegativePowerNormSimilarity(Similarity):
    """Negative power norm: -\\|x - y\\|_p^p."""

    def __init__(self, p: 'Union[int, float]'=2):
        """
        Initialize the similarity.

        :param p:
            The parameter p for the p-norm.
        """
        super().__init__()
        self.p = p

    def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        return -(x.unsqueeze(dim=-2) - y.unsqueeze(dim=-3)).pow(self.p).sum(dim
            =-1)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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 import nn
from abc import abstractmethod
from typing import Union
import torch.utils.data
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_neg_pow_sub_sum_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
    x3 = xindex // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = -tmp18
    tl.store(out_ptr0 + x4, tmp19, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_neg_pow_sub_sum_0[grid(256)](arg0_1, arg1_1, buf0,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class Similarity(nn.Module):
    """Base class for similarity functions."""

    @abstractmethod
    def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Compute pair-wise similarities.

        :param x: shape: (*, n, d)
            The first vectors.
        :param y: shape: (*, m, d)
            The second vectors.

        :return: shape: (*, n, m)
            The similarity values.
        """
        raise NotImplementedError

    def one_to_one(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Compute batched one-to-one similarities.

        :param x: shape: (*, d)
            The first vectors.
        :param y: shape: (*, d)
            The second vectors.

        :return: shape: (*)
            The similarity values.
        """
        return self(x.unsqueeze(dim=-2), y.unsqueeze(dim=-2)).squeeze(dim=-1
            ).squeeze(dim=-1)


class NegativePowerNormSimilarityNew(Similarity):
    """Negative power norm: -\\|x - y\\|_p^p."""

    def __init__(self, p: 'Union[int, float]'=2):
        """
        Initialize the similarity.

        :param p:
            The parameter p for the p-norm.
        """
        super().__init__()
        self.p = p

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
