# 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_4/inductor_cache/ts/ctstfkrc7paolmsqy6y4oodrx7y7zl43vevbfxmce3etmgnfetoi.py
# Topologically Sorted Source Nodes: [mean, sub, std, z1_norm], Original ATen: [aten.mean, aten.sub, aten.std, aten.div]
# Source node to ATen node mapping:
#   mean => mean
#   std => sqrt, var
#   sub => sub
#   z1_norm => div
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [0]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %mean), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%arg0_1, [0]), kwargs = {correction: 1.0})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt), kwargs = {})
triton_poi_fused_div_mean_std_sub_0 = async_compile.triton('triton_poi_fused_div_mean_std_sub_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=[16], 
    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_div_mean_std_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_div_mean_std_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = 3.0
    tmp23 = tmp21 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp10 / tmp24
    tl.store(out_ptr0 + (x2), tmp25, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2b/c2bijcimj2gqjrx4memimto7u2q4w3labsdqg2pqksqyyxv6rdzt.py
# Topologically Sorted Source Nodes: [cross_corr], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   cross_corr => div_2
# Graph fragment:
#   %div_2 : [num_users=6] = call_function[target=torch.ops.aten.div.Tensor](args = (%mm, 4), kwargs = {})
triton_poi_fused_div_1 = async_compile.triton('triton_poi_fused_div_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: '*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_div_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_div_1(in_ptr0, 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 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/aq/caq2brq6igxa5rh44oq7gy67c57eo54qi7diazcnqyoudm3w5moo.py
# Topologically Sorted Source Nodes: [add_], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add_ => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%diagonal, -1), kwargs = {})
#   %copy__default : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%diagonal_default, %add), kwargs = {})
triton_poi_fused_add_2 = async_compile.triton('triton_poi_fused_add_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.pointwise(
    size_hints=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_2', 'mutated_arg_names': ['out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (5*x0), xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -1.0
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + (5*x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/wq/cwq4twtwxcsuyzn7bd2msejq5qdky2webkn3ednjowusbxy3yrcn.py
# Topologically Sorted Source Nodes: [pow_, on_diag], Original ATen: [aten.pow, aten.sum]
# Source node to ATen node mapping:
#   on_diag => sum_1
#   pow_ => pow_1
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%diagonal_1, 2), kwargs = {})
#   %copy__default_1 : [num_users=0] = call_function[target=torch.ops.aten.copy_.default](args = (%diagonal_default_1, %pow_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%diagonal_2,), kwargs = {})
triton_per_fused_pow_sum_3 = async_compile.triton('triton_per_fused_pow_sum_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.DEFAULT,
    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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_pow_sum_3', 'mutated_arg_names': ['in_ptr0', 'out_ptr1'], '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_pow_sum_3(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (5*r0), None, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tl.store(out_ptr1 + (tl.broadcast_to(5*r0, [XBLOCK, RBLOCK])), tmp1, None)
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/d2/cd2zqqhktmkamh5rxsdhx54vbx6azq47tux4tor3hdkrzfmnkj4l.py
# Topologically Sorted Source Nodes: [off_diag, mul, add], Original ATen: [aten.sum, aten.mul, aten.add]
# Source node to ATen node mapping:
#   add => add_1
#   mul => mul
#   off_diag => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view_6,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 0.005), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mul), kwargs = {})
triton_per_fused_add_mul_sum_4 = async_compile.triton('triton_per_fused_add_mul_sum_4', '''
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, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mul_sum_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_add_mul_sum_4(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (1 + (5*(r0 // 4)) + (5*((r0 % 4) // 4)) + (r0 % 4)), rmask, other=0.0)
    tmp6 = tl.load(in_out_ptr0 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, 1])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp8 = 0.005
    tmp9 = tmp5 * tmp8
    tmp10 = tmp7 + tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp10, None)
''', 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, sub, std, z1_norm], Original ATen: [aten.mean, aten.sub, aten.std, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_mean_std_sub_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, sub_1, std_1, z2_norm], Original ATen: [aten.mean, aten.sub, aten.std, aten.div]
        triton_poi_fused_div_mean_std_sub_0.run(arg1_1, buf1, 16, grid=grid(16), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean_1, sub_1, std_1, z2_norm, matmul], Original ATen: [aten.mean, aten.sub, aten.std, aten.div, aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (4, 4), (1, 4), 0), buf1, out=buf2)
        del buf0
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [cross_corr], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(buf2, buf3, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [add_], Original ATen: [aten.add]
        triton_poi_fused_add_2.run(buf2, buf3, 4, grid=grid(4), stream=stream0)
        del buf2
        buf7 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [pow_, on_diag], Original ATen: [aten.pow, aten.sum]
        triton_per_fused_pow_sum_3.run(buf3, buf3, buf7, 1, 4, grid=grid(1), stream=stream0)
        buf9 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [off_diag, mul, add], Original ATen: [aten.sum, aten.mul, aten.add]
        triton_per_fused_add_mul_sum_4.run(buf9, buf3, 1, 12, grid=grid(1), stream=stream0)
        del buf3
    return (buf9, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (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
import torch.nn as nn


class BarlowTwinsLoss(nn.Module):

    def __init__(self, batch_size, lambda_coeff=0.005, z_dim=128):
        super().__init__()
        self.z_dim = z_dim
        self.batch_size = batch_size
        self.lambda_coeff = lambda_coeff

    def off_diagonal_ele(self, x):
        n, m = x.shape
        assert n == m
        return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()

    def forward(self, z1, z2):
        z1_norm = (z1 - torch.mean(z1, dim=0)) / torch.std(z1, dim=0)
        z2_norm = (z2 - torch.mean(z2, dim=0)) / torch.std(z2, dim=0)
        cross_corr = torch.matmul(z1_norm.T, z2_norm) / self.batch_size
        on_diag = torch.diagonal(cross_corr).add_(-1).pow_(2).sum()
        off_diag = self.off_diagonal_ele(cross_corr).pow_(2).sum()
        return on_diag + self.lambda_coeff * off_diag


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


def get_init_inputs():
    return [[], {'batch_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
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_div_mean_std_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = 3.0
    tmp23 = tmp21 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp10 / tmp24
    tl.store(out_ptr0 + x2, tmp25, xmask)


@triton.jit
def triton_poi_fused_div_1(in_ptr0, 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 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 5 * x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -1.0
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + 5 * x0, tmp4, xmask)


@triton.jit
def triton_per_fused_pow_sum_3(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 5 * r0, None, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tl.store(out_ptr1 + tl.broadcast_to(5 * r0, [XBLOCK, RBLOCK]), tmp1, None)
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp4, None)


@triton.jit
def triton_per_fused_add_mul_sum_4(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (1 + 5 * (r0 // 4) + 5 * (r0 % 4 // 4) + r0 % 
        4), rmask, other=0.0)
    tmp6 = tl.load(in_out_ptr0 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, 1])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp8 = 0.005
    tmp9 = tmp5 * tmp8
    tmp10 = tmp7 + tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp10, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mean_std_sub_0[grid(16)](arg0_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_mean_std_sub_0[grid(16)](arg1_1, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (4, 4), (1, 4), 0), buf1,
            out=buf2)
        del buf0
        buf3 = buf1
        del buf1
        triton_poi_fused_div_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        triton_poi_fused_add_2[grid(4)](buf2, buf3, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
        del buf2
        buf7 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_pow_sum_3[grid(1)](buf3, buf3, buf7, 1, 4, XBLOCK=
            1, num_warps=2, num_stages=1)
        buf9 = buf7
        del buf7
        triton_per_fused_add_mul_sum_4[grid(1)](buf9, buf3, 1, 12, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf3
    return buf9,


class BarlowTwinsLossNew(nn.Module):

    def __init__(self, batch_size, lambda_coeff=0.005, z_dim=128):
        super().__init__()
        self.z_dim = z_dim
        self.batch_size = batch_size
        self.lambda_coeff = lambda_coeff

    def off_diagonal_ele(self, x):
        n, m = x.shape
        assert n == m
        return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()

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