# 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_7/inductor_cache/6n/c6nzxribncukxumzo6sd4qteovnhn4lgur4ekbupb7htju2zqwug.py
# Topologically Sorted Source Nodes: [on_diag, pow_1, on_diag_1], Original ATen: [aten.add, aten.pow, aten.sum]
# Source node to ATen node mapping:
#   on_diag => add
#   on_diag_1 => sum_1
#   pow_1 => pow_1
# 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 = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%diagonal_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
triton_per_fused_add_pow_sum_0 = async_compile.triton('triton_per_fused_add_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.persistent_reduction(
    size_hints=[1, 64],
    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, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_pow_sum_0', '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_add_pow_sum_0(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 + (65*r0), None, eviction_policy='evict_last')
    tmp1 = -1.0
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr1 + (tl.broadcast_to(65*r0, [XBLOCK, RBLOCK])), tmp2, None)
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp6, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/u2/cu2q6xanzhzdl7vgbbpwx7gx4ge6d2wysmm5vs4wjx6a2ddbtkae.py
# Topologically Sorted Source Nodes: [pow_2, off_diag_1, mul, loss, loss_1], Original ATen: [aten.pow, aten.sum, aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   loss => add_1
#   loss_1 => div
#   mul => mul
#   off_diag_1 => sum_2
#   pow_2 => pow_2
# Graph fragment:
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%view_5, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_2,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_2, 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mul), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, 4096), kwargs = {})
triton_red_fused_add_div_mul_pow_sum_1 = async_compile.triton('triton_red_fused_add_div_mul_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.reduction(
    size_hints=[1, 4096],
    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, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_div_mul_pow_sum_1', '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_red_fused_add_div_mul_pow_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4032
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        tmp0 = tl.load(in_ptr0 + (1 + (65*(r0 // 64)) + (r0 % 64)), rmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tmp5 = tl.load(in_out_ptr0 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, 1])
    tmp7 = 1.0
    tmp8 = tmp3 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = 0.000244140625
    tmp11 = tmp9 * tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [c], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(arg0_1, (64, 2), (1, 128), 0), reinterpret_tensor(arg0_1, (2, 64), (128, 1), 64), out=buf0)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [on_diag, pow_1, on_diag_1], Original ATen: [aten.add, aten.pow, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_pow_sum_0.run(buf0, buf0, buf3, 1, 64, grid=grid(1), stream=stream0)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [pow_2, off_diag_1, mul, loss, loss_1], Original ATen: [aten.pow, aten.sum, aten.mul, aten.add, aten.div]
        triton_red_fused_add_div_mul_pow_sum_1.run(buf5, buf0, 1, 4032, grid=grid(1), stream=stream0)
        del buf0
    return (buf5, )


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)
    fn = lambda: call([arg0_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


class PairwiseCrossCorrelation(nn.Module):

    def __init__(self, lambd=1):
        super().__init__()
        self.lambd = lambd

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

    def forward(self, inp, target=None):
        inp = inp.flatten(1)
        assert len(inp) % 2 == 0
        samples1, samples2 = inp[::2], inp[1::2]
        c = samples1.T @ samples2
        on_diag = torch.diagonal(c).add_(-1)
        on_diag = torch.pow(on_diag, 2).sum()
        off_diag = self.off_diagonal(c)
        off_diag = torch.pow(off_diag, 2).sum()
        loss = on_diag + self.lambd * off_diag
        loss = loss / len(c) ** 2
        return loss


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


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

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 import 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_per_fused_add_pow_sum_0(in_ptr0, out_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 + 65 * r0, None, eviction_policy='evict_last')
    tmp1 = -1.0
    tmp2 = tmp0 + tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tl.store(out_ptr1 + tl.broadcast_to(65 * r0, [XBLOCK, RBLOCK]), tmp2, None)
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)


@triton.jit
def triton_red_fused_add_div_mul_pow_sum_1(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 4032
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp3 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        tmp0 = tl.load(in_ptr0 + (1 + 65 * (r0 // 64) + r0 % 64), rmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0 * tmp0
        tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
        tmp4 = _tmp3 + tmp2
        _tmp3 = tl.where(rmask, tmp4, _tmp3)
    tmp3 = tl.sum(_tmp3, 1)[:, None]
    tmp5 = tl.load(in_out_ptr0 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, 1])
    tmp7 = 1.0
    tmp8 = tmp3 * tmp7
    tmp9 = tmp6 + tmp8
    tmp10 = 0.000244140625
    tmp11 = tmp9 * tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (64, 2), (1, 128), 0),
            reinterpret_tensor(arg0_1, (2, 64), (128, 1), 64), out=buf0)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_pow_sum_0[grid(1)](buf0, buf0, buf3, 1, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf5 = buf3
        del buf3
        triton_red_fused_add_div_mul_pow_sum_1[grid(1)](buf5, buf0, 1, 4032,
            XBLOCK=1, RBLOCK=2048, num_warps=16, num_stages=1)
        del buf0
    return buf5,


class PairwiseCrossCorrelationNew(nn.Module):

    def __init__(self, lambd=1):
        super().__init__()
        self.lambd = lambd

    def off_diagonal(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):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
