# 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/z4/cz46z3dq5qodaimel7dv62vekmpk4ksq7th54olssudfutixvgvi.py
# Topologically Sorted Source Nodes: [max_1, max_val], Original ATen: [aten.max]
# Source node to ATen node mapping:
#   max_1 => max_1
#   max_val => max_2
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%diagonal, 2), kwargs = {})
#   %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%getitem,), kwargs = {})
triton_per_fused_max_0 = async_compile.triton('triton_per_fused_max_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, 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, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (16*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (5 + (16*r0)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (10 + (16*r0)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (15 + (16*r0)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = triton_helpers.max2(tmp7, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/an/can3q5iicacaiarr3s3lhazp34y4o7vrl4aujiyo4cuyuo56ndle.py
# Topologically Sorted Source Nodes: [mul, min_val], Original ATen: [aten.mul, aten.max]
# Source node to ATen node mapping:
#   min_val => max_3
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, -1), kwargs = {})
#   %max_3 : [num_users=1] = call_function[target=torch.ops.aten.max.default](args = (%mul,), kwargs = {})
triton_per_fused_max_mul_1 = async_compile.triton('triton_per_fused_max_mul_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: '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_per_fused_max_mul_1', 'mutated_arg_names': [], 'no_x_dim': True, '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_max_mul_1(in_ptr0, out_ptr0, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = -1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/iw/ciwzrjqk4lzlt6b4l3t5t2q37pukslnoccrywczshjiufk5kfpky.py
# Topologically Sorted Source Nodes: [sub, max_4], Original ATen: [aten.sub, aten.max]
# Source node to ATen node mapping:
#   max_4 => max_4
#   sub => sub
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %unsqueeze_3), kwargs = {})
#   %max_4 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%sub, 2), kwargs = {})
triton_poi_fused_max_sub_2 = async_compile.triton('triton_poi_fused_max_sub_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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_max_sub_2(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
    tmp4 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (0))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp10 = tl.load(in_ptr2 + (0))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp16 = tl.load(in_ptr0 + (4 + x0 + (16*x1)), xmask)
    tmp19 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (8 + x0 + (16*x1)), xmask)
    tmp28 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr0 + (12 + x0 + (16*x1)), xmask)
    tmp37 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = x0
    tmp3 = tmp1 == tmp2
    tmp5 = 0.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp6 * tmp5
    tmp12 = tmp9 + tmp11
    tmp13 = tl_math.abs(tmp12)
    tmp14 = tmp7 + tmp13
    tmp15 = tmp0 - tmp14
    tmp17 = tl.full([1], 1, tl.int64)
    tmp18 = tmp17 == tmp2
    tmp20 = tl.where(tmp18, tmp19, tmp5)
    tmp21 = tmp20 * tmp5
    tmp22 = tmp21 + tmp13
    tmp23 = tmp16 - tmp22
    tmp24 = triton_helpers.maximum(tmp15, tmp23)
    tmp26 = tl.full([1], 2, tl.int64)
    tmp27 = tmp26 == tmp2
    tmp29 = tl.where(tmp27, tmp28, tmp5)
    tmp30 = tmp29 * tmp5
    tmp31 = tmp30 + tmp13
    tmp32 = tmp25 - tmp31
    tmp33 = triton_helpers.maximum(tmp24, tmp32)
    tmp35 = tl.full([1], 3, tl.int64)
    tmp36 = tmp35 == tmp2
    tmp38 = tl.where(tmp36, tmp37, tmp5)
    tmp39 = tmp38 * tmp5
    tmp40 = tmp39 + tmp13
    tmp41 = tmp34 - tmp40
    tmp42 = triton_helpers.maximum(tmp33, tmp41)
    tl.store(out_ptr0 + (x2), tmp42, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/3c/c3cddeoyu6jmox52voje7xldnnecnaowb53oavkcdosdyg2rjyck.py
# Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   cat => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%getitem, %getitem_4], 1), kwargs = {})
triton_poi_fused_cat_3 = async_compile.triton('triton_poi_fused_cat_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=[32], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = (xindex // 8)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((16*x0) + (64*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (5 + (16*x0) + (64*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.load(in_ptr0 + (10 + (16*x0) + (64*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tl.load(in_ptr0 + (15 + (16*x0) + (64*x1)), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tmp15 = tl.full([1], 8, tl.int64)
    tmp16 = tmp0 < tmp15
    tmp17 = tl.load(in_ptr1 + ((4*((-4) + x0)) + (16*x1)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr1 + (1 + (4*((-4) + x0)) + (16*x1)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp20 = tl.load(in_ptr1 + (2 + (4*((-4) + x0)) + (16*x1)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = tl.load(in_ptr1 + (3 + (4*((-4) + x0)) + (16*x1)), tmp14 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = triton_helpers.maximum(tmp21, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp14, tmp23, tmp24)
    tmp26 = tl.where(tmp4, tmp13, tmp25)
    tl.store(out_ptr0 + (x2), tmp26, xmask)
''', 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((), (), torch.float32)
        # Topologically Sorted Source Nodes: [max_1, max_val], Original ATen: [aten.max]
        stream0 = get_raw_stream(0)
        triton_per_fused_max_0.run(arg0_1, buf0, 1, 16, grid=grid(1), stream=stream0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [mul, min_val], Original ATen: [aten.mul, aten.max]
        triton_per_fused_max_mul_1.run(arg0_1, buf1, 1, 256, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, max_4], Original ATen: [aten.sub, aten.max]
        triton_poi_fused_max_sub_2.run(arg0_1, buf0, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat], Original ATen: [aten.cat]
        triton_poi_fused_cat_3.run(arg0_1, buf2, buf3, 32, grid=grid(32), stream=stream0)
        del arg0_1
        del buf2
    return (buf3, )


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


class diag_offdiag_maxpool(torch.nn.Module):
    """diag_offdiag_maxpool"""

    def __init__(self):
        super(diag_offdiag_maxpool, self).__init__()

    def forward(self, inputs):
        max_diag = torch.max(torch.diagonal(inputs, dim1=-2, dim2=-1), dim=2)[0
            ]
        max_val = torch.max(max_diag)
        min_val = torch.max(torch.mul(inputs, -1))
        val = torch.abs(max_val + min_val)
        min_mat = torch.unsqueeze(torch.unsqueeze(torch.diagonal(torch.add(
            torch.mul(torch.diag_embed(inputs[0][0]), 0), val)), dim=0), dim=0)
        max_offdiag = torch.max(torch.max(torch.sub(inputs, min_mat), dim=2
            )[0], dim=2)[0]
        return torch.cat((max_diag, max_offdiag), dim=1)


def get_inputs():
    return [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._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_max_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 16 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (5 + 16 * r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (10 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (15 + 16 * r0), None, eviction_policy='evict_last'
        )
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = triton_helpers.max2(tmp7, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp9, None)


@triton.jit
def triton_per_fused_max_mul_1(in_ptr0, out_ptr0, 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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = -1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)


@triton.jit
def triton_poi_fused_max_sub_2(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp10 = tl.load(in_ptr2 + 0)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp16 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp28 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp34 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp37 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = x0
    tmp3 = tmp1 == tmp2
    tmp5 = 0.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp6 * tmp5
    tmp12 = tmp9 + tmp11
    tmp13 = tl_math.abs(tmp12)
    tmp14 = tmp7 + tmp13
    tmp15 = tmp0 - tmp14
    tmp17 = tl.full([1], 1, tl.int64)
    tmp18 = tmp17 == tmp2
    tmp20 = tl.where(tmp18, tmp19, tmp5)
    tmp21 = tmp20 * tmp5
    tmp22 = tmp21 + tmp13
    tmp23 = tmp16 - tmp22
    tmp24 = triton_helpers.maximum(tmp15, tmp23)
    tmp26 = tl.full([1], 2, tl.int64)
    tmp27 = tmp26 == tmp2
    tmp29 = tl.where(tmp27, tmp28, tmp5)
    tmp30 = tmp29 * tmp5
    tmp31 = tmp30 + tmp13
    tmp32 = tmp25 - tmp31
    tmp33 = triton_helpers.maximum(tmp24, tmp32)
    tmp35 = tl.full([1], 3, tl.int64)
    tmp36 = tmp35 == tmp2
    tmp38 = tl.where(tmp36, tmp37, tmp5)
    tmp39 = tmp38 * tmp5
    tmp40 = tmp39 + tmp13
    tmp41 = tmp34 - tmp40
    tmp42 = triton_helpers.maximum(tmp33, tmp41)
    tl.store(out_ptr0 + x2, tmp42, xmask)


@triton.jit
def triton_poi_fused_cat_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (16 * x0 + 64 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tl.load(in_ptr0 + (5 + 16 * x0 + 64 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.load(in_ptr0 + (10 + 16 * x0 + 64 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp9 = triton_helpers.maximum(tmp7, tmp8)
    tmp10 = tl.load(in_ptr0 + (15 + 16 * x0 + 64 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tl.full(tmp11.shape, 0.0, tmp11.dtype)
    tmp13 = tl.where(tmp4, tmp11, tmp12)
    tmp14 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp17 = tl.load(in_ptr1 + (4 * (-4 + x0) + 16 * x1), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp18 = tl.load(in_ptr1 + (1 + 4 * (-4 + x0) + 16 * x1), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp20 = tl.load(in_ptr1 + (2 + 4 * (-4 + x0) + 16 * x1), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp21 = triton_helpers.maximum(tmp19, tmp20)
    tmp22 = tl.load(in_ptr1 + (3 + 4 * (-4 + x0) + 16 * x1), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = triton_helpers.maximum(tmp21, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp14, tmp23, tmp24)
    tmp26 = tl.where(tmp4, tmp13, tmp25)
    tl.store(out_ptr0 + x2, tmp26, xmask)


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((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_max_0[grid(1)](arg0_1, buf0, 1, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf1 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_max_mul_1[grid(1)](arg0_1, buf1, 1, 256, num_warps
            =2, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_max_sub_2[grid(64)](arg0_1, buf0, buf1, buf2, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_3[grid(32)](arg0_1, buf2, buf3, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del arg0_1
        del buf2
    return buf3,


class diag_offdiag_maxpoolNew(torch.nn.Module):
    """diag_offdiag_maxpool"""

    def __init__(self):
        super(diag_offdiag_maxpoolNew, self).__init__()

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