# 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/v2/cv2ohk4vpat6jimblbxldlcmj6xwbdtpffu3zhdixmnvm47unics.py
# Topologically Sorted Source Nodes: [sub, pow_1, distances], Original ATen: [aten.sub, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   distances => sum_1
#   pow_1 => pow_1
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [1]), kwargs = {})
triton_poi_fused_pow_sub_sum_0 = async_compile.triton('triton_poi_fused_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=[64], 
    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_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_pow_sub_sum_0(in_ptr0, in_ptr1, 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)
    tmp1 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp10 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    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
    tl.store(out_ptr0 + (x2), tmp18, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/zr/czr2vrtmp2zzmoldsxojgyuiyfeueaymvvtixpuppz45iypjtjb3.py
# Topologically Sorted Source Nodes: [mul, mul_1, add, add_1, sqrt, sub_1, relu, pow_2, mul_2, add_2, losses, mean], Original ATen: [aten.mul, aten.add, aten.sqrt, aten.rsub, aten.relu, aten.pow, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   losses => mul_3
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_2 => pow_2
#   relu => relu
#   sqrt => sqrt
#   sub_1 => sub_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, %sum_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg2_1, -1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-09), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (4, %sqrt), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %pow_2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 0.5), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_3,), kwargs = {})
triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_1 = async_compile.triton('triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_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.persistent_reduction(
    size_hints=[1, 256],
    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=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp1 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = -1.0
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp4 + tmp5
    tmp7 = 1e-09
    tmp8 = tmp1 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 4.0
    tmp11 = tmp10 - tmp9
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tmp13 * tmp13
    tmp15 = tmp6 * tmp14
    tmp16 = tmp2 + tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = 256.0
    tmp23 = tmp21 / tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp23, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, distances], Original ATen: [aten.sub, aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_pow_sub_sum_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [mul, mul_1, add, add_1, sqrt, sub_1, relu, pow_2, mul_2, add_2, losses, mean], Original ATen: [aten.mul, aten.add, aten.sqrt, aten.rsub, aten.relu, aten.pow, aten.mean]
        triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_1.run(buf2, arg2_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg2_1
        del buf0
    return (buf2, )


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)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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
import torch.nn as nn
from torch.nn import functional as F


class ContrastiveLoss(nn.Module):
    """
    Contrastive loss
    Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
    """

    def __init__(self, margin):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin
        self.eps = 1e-09

    def forward(self, output1, output2, target, size_average=True):
        distances = (output2 - output1).pow(2).sum(1)
        losses = 0.5 * (target.float() * distances + (1 + -1 * target).
            float() * F.relu(self.margin - (distances + self.eps).sqrt()).
            pow(2))
        return losses.mean() if size_average else losses.sum()


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


def get_init_inputs():
    return [[], {'margin': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
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_pow_sub_sum_0(in_ptr0, in_ptr1, 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)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    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
    tl.store(out_ptr0 + x2, tmp18, xmask)


@triton.jit
def triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = -1.0
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp4 + tmp5
    tmp7 = 1e-09
    tmp8 = tmp1 + tmp7
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 4.0
    tmp11 = tmp10 - tmp9
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp14 = tmp13 * tmp13
    tmp15 = tmp6 * tmp14
    tmp16 = tmp2 + tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = 256.0
    tmp23 = tmp21 / tmp22
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_sub_sum_0[grid(64)](arg0_1, arg1_1, buf0, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_add_mean_mul_pow_relu_rsub_sqrt_1[grid(1)](buf2,
            arg2_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg2_1
        del buf0
    return buf2,


class ContrastiveLossNew(nn.Module):
    """
    Contrastive loss
    Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise
    """

    def __init__(self, margin):
        super(ContrastiveLossNew, self).__init__()
        self.margin = margin
        self.eps = 1e-09

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
