# 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/bi/cbibg4lc7dc2sbbxe7z4v4ebtulhxgixrw57yvrp2yyfgel2ghb5.py
# Topologically Sorted Source Nodes: [sum_1, itruediv], Original ATen: [aten.sum, aten.div]
# Source node to ATen node mapping:
#   itruediv => div
#   sum_1 => sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select, %sum_1), kwargs = {})
triton_per_fused_div_sum_0 = async_compile.triton('triton_per_fused_div_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, 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_div_sum_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_div_sum_0(in_ptr0, out_ptr1, 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 + (r0), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = tmp8 / tmp11
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp12, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/o4/co4t5cpqvwzgwid4gtpymik6alub22ihe334fobcw5otrmsoxavz.py
# Topologically Sorted Source Nodes: [itruediv], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   itruediv => div
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select, %sum_1), kwargs = {})
#   %select_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%view_1, %div, 0, 0), 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=[64], 
    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_div_1', '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_1(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
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp9 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = 4.0
    tmp12 = tmp10 / tmp11
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/xf/cxfi22rac3t4r7mazbza6uabzf5oenc5fn7fw2a7l7czenb5tshs.py
# Topologically Sorted Source Nodes: [sum_2, sum_3, itruediv_2], Original ATen: [aten.sum, aten.div]
# Source node to ATen node mapping:
#   itruediv_2 => div_2
#   sum_2 => sum_2
#   sum_3 => sum_3
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_8,), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_16,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_17, %sum_3), kwargs = {})
triton_per_fused_div_sum_2 = async_compile.triton('triton_per_fused_div_sum_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.persistent_reduction(
    size_hints=[1, 16],
    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=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_div_sum_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 2, '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_div_sum_2(in_ptr0, out_ptr0, out_ptr2, 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
    tmp3 = tl.load(in_ptr0 + (r0 + (4*((r0 % 4) // 4))), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + (4*((r0 % 4) // 4))), None)
    tmp12 = tl.load(in_ptr0 + (r0 + (8*((r0 % 4) // 4))), None)
    tmp13 = tl.load(in_ptr0 + (16 + r0 + (8*((r0 % 4) // 4))), None)
    tmp18 = tl.load(in_ptr0 + (32 + r0 + (8*((r0 % 4) // 4))), None)
    tmp0 = tl.full([1, 1], 1, tl.int32)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp9 = tl.full([1, 1], 2, tl.int32)
    tmp10 = tmp9 == tmp0
    tmp11 = tmp0 == tmp0
    tmp14 = tl.where(tmp2, tmp12, tmp13)
    tmp15 = tmp14 / tmp8
    tmp16 = tl.where(tmp11, tmp15, tmp14)
    tmp17 = tmp9 == tmp1
    tmp19 = tl.where(tmp17, tmp12, tmp18)
    tmp20 = tl.where(tmp10, tmp15, tmp19)
    tmp21 = tl.where(tmp10, tmp16, tmp20)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tmp21 / tmp24
    tl.store(out_ptr2 + (tl.broadcast_to(r0, [XBLOCK, RBLOCK])), tmp25, None)
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tp/ctp65xzcaq3yt6j5igmjpdwgic52mltna4dnuwk4o2jezwaqqwcq.py
# Topologically Sorted Source Nodes: [itruediv_2], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   itruediv_2 => div_2
# Graph fragment:
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select_17, %sum_3), kwargs = {})
#   %select_scatter_default_4 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%view_19, %div_2, 0, 2), kwargs = {})
triton_poi_fused_div_3 = async_compile.triton('triton_poi_fused_div_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=[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_div_3', '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_3(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
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (x0 + (8*((x0 % 4) // 4))), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (16 + x0 + (8*((x0 % 4) // 4))), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (0))
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK])
    tmp17 = tl.load(in_ptr1 + (x0 + (8*((x0 % 4) // 4)) + (16*x1)), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 1, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = tmp4 == tmp4
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = tmp4 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp14 = tmp11 / tmp13
    tmp15 = tl.where(tmp6, tmp14, tmp11)
    tmp16 = tmp0 == tmp7
    tmp18 = tl.where(tmp16, tmp9, tmp17)
    tmp19 = tl.where(tmp5, tmp14, tmp18)
    tmp20 = tl.where(tmp5, tmp15, tmp19)
    tmp21 = tl.where(tmp2, tmp3, tmp20)
    tl.store(out_ptr0 + (x2), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/wl/cwlv3v7i5tdm7rgttqnrgro6gjlltqdiyasmyr7rg7nydh3gicwq.py
# Topologically Sorted Source Nodes: [sum_4], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   sum_4 => sum_4
# Graph fragment:
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%select_24,), kwargs = {})
triton_per_fused_sum_4 = async_compile.triton('triton_per_fused_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, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_sum_4', 'mutated_arg_names': [], '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_sum_4(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
    tmp3 = tl.load(in_ptr0 + (32 + r0 + (4*((r0 % 4) // 4))), None)
    tmp4 = tl.load(in_ptr0 + (48 + r0 + (4*((r0 % 4) // 4))), None)
    tmp0 = tl.full([1, 1], 3, tl.int32)
    tmp1 = tl.full([1, 1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jj/cjjqjoipyiqihfkftp47jgsxj24bv47ixmjupvxwkuttwgjj6aii.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %select_scatter_default_7 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%view_32, %select_26, 0, 3), kwargs = {})
triton_poi_fused_5 = async_compile.triton('triton_poi_fused_5', '''
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=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_5', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_5(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
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp6 = tl.load(in_ptr0 + (32 + x0 + (8*((x0 % 4) // 4))), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (48 + x0 + (8*((x0 % 4) // 4))), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (0))
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (x0 + (8*((x0 % 4) // 4)) + (16*x1)), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp4 = tl.full([1], 2, tl.int32)
    tmp5 = tmp1 == tmp4
    tmp8 = tl.where(tmp5, tmp6, tmp7)
    tmp11 = tmp8 / tmp10
    tmp12 = tl.where(tmp3, tmp11, tmp8)
    tmp13 = tmp0 == tmp4
    tmp15 = tl.where(tmp13, tmp6, tmp14)
    tmp16 = tl.where(tmp2, tmp11, tmp15)
    tmp17 = tl.where(tmp2, tmp12, tmp16)
    tl.store(out_ptr0 + (x2), tmp17, 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)
        buf1 = empty_strided_cuda((16, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [sum_1, itruediv], Original ATen: [aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_div_sum_0.run(arg0_1, buf1, 1, 16, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [itruediv], Original ATen: [aten.div]
        triton_poi_fused_div_1.run(buf1, arg0_1, buf2, 64, grid=grid(64), stream=stream0)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [sum_2, sum_3, itruediv_2], Original ATen: [aten.sum, aten.div]
        triton_per_fused_div_sum_2.run(buf2, buf3, buf5, 1, 16, grid=grid(1), stream=stream0)
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [itruediv_2], Original ATen: [aten.div]
        triton_poi_fused_div_3.run(buf5, buf2, buf3, buf6, 64, grid=grid(64), stream=stream0)
        del buf5
        buf7 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [sum_4], Original ATen: [aten.sum]
        triton_per_fused_sum_4.run(buf6, buf7, 1, 16, grid=grid(1), stream=stream0)
        buf8 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_5.run(buf6, buf7, buf8, 64, grid=grid(64), stream=stream0)
        del buf6
        del buf7
    return (reinterpret_tensor(buf8, (4, 1, 4, 4), (16, 16, 4, 1), 0), )


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
import torch.nn as nn


class SpatialAttn(nn.Module):
    """Spatial Attention Layer"""

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

    def forward(self, x):
        x = x.mean(1, keepdim=True)
        h = x.size(2)
        w = x.size(3)
        x = x.view(x.size(0), -1)
        z = x
        for b in range(x.size(0)):
            z[b] /= torch.sum(z[b])
        z = z.view(x.size(0), 1, h, w)
        return z


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
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_per_fused_div_sum_0(in_ptr0, out_ptr1, 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 + r0, None)
    tmp1 = tl.load(in_ptr0 + (16 + r0), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = tmp8 / tmp11
    tl.store(out_ptr1 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp12, None)


@triton.jit
def triton_poi_fused_div_1(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
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = 4.0
    tmp12 = tmp10 / tmp11
    tmp13 = tl.where(tmp2, tmp3, tmp12)
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_per_fused_div_sum_2(in_ptr0, out_ptr0, out_ptr2, 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
    tmp3 = tl.load(in_ptr0 + (r0 + 4 * (r0 % 4 // 4)), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 4 * (r0 % 4 // 4)), None)
    tmp12 = tl.load(in_ptr0 + (r0 + 8 * (r0 % 4 // 4)), None)
    tmp13 = tl.load(in_ptr0 + (16 + r0 + 8 * (r0 % 4 // 4)), None)
    tmp18 = tl.load(in_ptr0 + (32 + r0 + 8 * (r0 % 4 // 4)), None)
    tmp0 = tl.full([1, 1], 1, tl.int32)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tmp9 = tl.full([1, 1], 2, tl.int32)
    tmp10 = tmp9 == tmp0
    tmp11 = tmp0 == tmp0
    tmp14 = tl.where(tmp2, tmp12, tmp13)
    tmp15 = tmp14 / tmp8
    tmp16 = tl.where(tmp11, tmp15, tmp14)
    tmp17 = tmp9 == tmp1
    tmp19 = tl.where(tmp17, tmp12, tmp18)
    tmp20 = tl.where(tmp10, tmp15, tmp19)
    tmp21 = tl.where(tmp10, tmp16, tmp20)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tmp21 / tmp24
    tl.store(out_ptr2 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp25, None)
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_poi_fused_div_3(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
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (x0 + 8 * (x0 % 4 // 4)), xmask,
        eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (16 + x0 + 8 * (x0 % 4 // 4)), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + 0)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK])
    tmp17 = tl.load(in_ptr1 + (x0 + 8 * (x0 % 4 // 4) + 16 * x1), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 1, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp6 = tmp4 == tmp4
    tmp7 = tl.full([1], 0, tl.int32)
    tmp8 = tmp4 == tmp7
    tmp11 = tl.where(tmp8, tmp9, tmp10)
    tmp14 = tmp11 / tmp13
    tmp15 = tl.where(tmp6, tmp14, tmp11)
    tmp16 = tmp0 == tmp7
    tmp18 = tl.where(tmp16, tmp9, tmp17)
    tmp19 = tl.where(tmp5, tmp14, tmp18)
    tmp20 = tl.where(tmp5, tmp15, tmp19)
    tmp21 = tl.where(tmp2, tmp3, tmp20)
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_per_fused_sum_4(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
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 4 * (r0 % 4 // 4)), None)
    tmp4 = tl.load(in_ptr0 + (48 + r0 + 4 * (r0 % 4 // 4)), None)
    tmp0 = tl.full([1, 1], 3, tl.int32)
    tmp1 = tl.full([1, 1], 2, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.sum(tmp6, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp8, None)


@triton.jit
def triton_poi_fused_5(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
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 8 * (x0 % 4 // 4)), xmask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (48 + x0 + 8 * (x0 % 4 // 4)), xmask,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + 0)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (x0 + 8 * (x0 % 4 // 4) + 16 * x1), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp3 = tmp1 == tmp1
    tmp4 = tl.full([1], 2, tl.int32)
    tmp5 = tmp1 == tmp4
    tmp8 = tl.where(tmp5, tmp6, tmp7)
    tmp11 = tmp8 / tmp10
    tmp12 = tl.where(tmp3, tmp11, tmp8)
    tmp13 = tmp0 == tmp4
    tmp15 = tl.where(tmp13, tmp6, tmp14)
    tmp16 = tl.where(tmp2, tmp11, tmp15)
    tmp17 = tl.where(tmp2, tmp12, tmp16)
    tl.store(out_ptr0 + x2, tmp17, 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)
        buf1 = empty_strided_cuda((16,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_div_sum_0[grid(1)](arg0_1, buf1, 1, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_poi_fused_div_1[grid(64)](buf1, arg0_1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf1
        del buf1
        triton_per_fused_div_sum_2[grid(1)](buf2, buf3, buf5, 1, 16, XBLOCK
            =1, num_warps=2, num_stages=1)
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_poi_fused_div_3[grid(64)](buf5, buf2, buf3, buf6, 64, XBLOCK
            =64, num_warps=1, num_stages=1)
        del buf5
        buf7 = buf3
        del buf3
        triton_per_fused_sum_4[grid(1)](buf6, buf7, 1, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf8 = buf2
        del buf2
        triton_poi_fused_5[grid(64)](buf6, buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf6
        del buf7
    return reinterpret_tensor(buf8, (4, 1, 4, 4), (16, 16, 4, 1), 0),


class SpatialAttnNew(nn.Module):
    """Spatial Attention Layer"""

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

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