# 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/qz/cqza6p5fjiie2hfiu5dfjqqugrnzziwuwxzlhzy2aa7khopxjbym.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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=[256], 
    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__softmax_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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/x3/cx3at5njg54gyodxgpguamoryvolq7ekngws233hgttgu675e33m.py
# Topologically Sorted Source Nodes: [softmax, prediction, eq, eq_1, add, eq_2, float_1, sum_1, eq_3, float_2, sum_2, eq_4, eq_5, add_3, eq_6, float_3, sum_3, eq_7, float_4, sum_4, eq_8, eq_9, add_6, eq_10, float_5, sum_5, eq_11, float_6, sum_6, accs], Original ATen: [aten._softmax, aten.argmax, aten.eq, aten.add, aten._to_copy, aten.sum, aten.stack]
# Source node to ATen node mapping:
#   accs => cat
#   add => add
#   add_3 => add_3
#   add_6 => add_6
#   eq => eq
#   eq_1 => eq_1
#   eq_10 => eq_10
#   eq_11 => eq_11
#   eq_2 => eq_2
#   eq_3 => eq_3
#   eq_4 => eq_4
#   eq_5 => eq_5
#   eq_6 => eq_6
#   eq_7 => eq_7
#   eq_8 => eq_8
#   eq_9 => eq_9
#   float_1 => convert_element_type
#   float_2 => convert_element_type_1
#   float_3 => convert_element_type_2
#   float_4 => convert_element_type_3
#   float_5 => convert_element_type_4
#   float_6 => convert_element_type_5
#   prediction => argmax
#   softmax => div, sum_1
#   sum_1 => sum_2
#   sum_2 => sum_3
#   sum_3 => sum_4
#   sum_4 => sum_5
#   sum_5 => sum_6
#   sum_6 => sum_7
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %argmax : [num_users=3] = call_function[target=torch.ops.aten.argmax.default](args = (%div, 1), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%argmax, 1), kwargs = {})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select, 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%eq, %eq_1), kwargs = {})
#   %eq_2 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%add, 2), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq_2, torch.float32), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type,), kwargs = {})
#   %eq_3 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select, 1), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq_3, torch.float32), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type_1,), kwargs = {})
#   %eq_4 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%argmax, 2), kwargs = {})
#   %eq_5 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select_1, 1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%eq_4, %eq_5), kwargs = {})
#   %eq_6 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%add_3, 2), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq_6, torch.float32), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type_2,), kwargs = {})
#   %eq_7 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select_1, 1), kwargs = {})
#   %convert_element_type_3 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq_7, torch.float32), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type_3,), kwargs = {})
#   %eq_8 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%argmax, 3), kwargs = {})
#   %eq_9 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select_2, 1), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%eq_8, %eq_9), kwargs = {})
#   %eq_10 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%add_6, 2), kwargs = {})
#   %convert_element_type_4 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq_10, torch.float32), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type_4,), kwargs = {})
#   %eq_11 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%select_2, 1), kwargs = {})
#   %convert_element_type_5 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq_11, torch.float32), kwargs = {})
#   %sum_7 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%convert_element_type_5,), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2],), kwargs = {})
triton_per_fused__softmax__to_copy_add_argmax_eq_stack_sum_1 = async_compile.triton('triton_per_fused__softmax__to_copy_add_argmax_eq_stack_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.persistent_reduction(
    size_hints=[1, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: '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': {5: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 4, 6), equal_to_1=(5,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax__to_copy_add_argmax_eq_stack_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 6, '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__softmax__to_copy_add_argmax_eq_stack_sum_1(in_ptr0, in_ptr1, out_ptr7, out_ptr8, out_ptr9, 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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp55 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp66 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp76 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1, 1], 0, tl.int64)
    tmp18 = tl.full([1, 1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1, 1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1, 1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tmp52 = tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tmp54 = tmp53 == tmp18
    tmp56 = 1.0
    tmp57 = tmp55 == tmp56
    tmp58 = tmp54 | tmp57
    tmp59 = tmp58.to(tl.int64)
    tmp60 = tmp59 == tmp33
    tmp61 = tmp60.to(tl.float32)
    tmp62 = tl.broadcast_to(tmp61, [XBLOCK, RBLOCK])
    tmp64 = tl.sum(tmp62, 1)[:, None]
    tmp65 = tmp53 == tmp33
    tmp67 = tmp66 == tmp56
    tmp68 = tmp65 | tmp67
    tmp69 = tmp68.to(tl.int64)
    tmp70 = tmp69 == tmp33
    tmp71 = tmp70.to(tl.float32)
    tmp72 = tl.broadcast_to(tmp71, [XBLOCK, RBLOCK])
    tmp74 = tl.sum(tmp72, 1)[:, None]
    tmp75 = tmp53 == tmp48
    tmp77 = tmp76 == tmp56
    tmp78 = tmp75 | tmp77
    tmp79 = tmp78.to(tl.int64)
    tmp80 = tmp79 == tmp33
    tmp81 = tmp80.to(tl.float32)
    tmp82 = tl.broadcast_to(tmp81, [XBLOCK, RBLOCK])
    tmp84 = tl.sum(tmp82, 1)[:, None]
    tmp85 = tmp57.to(tl.float32)
    tmp86 = tl.broadcast_to(tmp85, [XBLOCK, RBLOCK])
    tmp88 = tl.sum(tmp86, 1)[:, None]
    tmp89 = tmp67.to(tl.float32)
    tmp90 = tl.broadcast_to(tmp89, [XBLOCK, RBLOCK])
    tmp92 = tl.sum(tmp90, 1)[:, None]
    tmp93 = tmp77.to(tl.float32)
    tmp94 = tl.broadcast_to(tmp93, [XBLOCK, RBLOCK])
    tmp96 = tl.sum(tmp94, 1)[:, None]
    tmp97 = tmp84 + tmp56
    tmp98 = tmp96 + tmp56
    tmp99 = tmp97 / tmp98
    tmp100 = 100.0
    tmp101 = tmp99 * tmp100
    tmp102 = tmp74 + tmp56
    tmp103 = tmp92 + tmp56
    tmp104 = tmp102 / tmp103
    tmp105 = tmp104 * tmp100
    tmp106 = tmp64 + tmp56
    tmp107 = tmp88 + tmp56
    tmp108 = tmp106 / tmp107
    tmp109 = tmp108 * tmp100
    tl.store(out_ptr7 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp101, None)
    tl.store(out_ptr8 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp105, None)
    tl.store(out_ptr9 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp109, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/s7/cs7iq6m5475mzwweftxc7y5pb74lnv7wdmyryfqnistrkyccwipi.py
# Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   mean => mean
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%cat,), kwargs = {})
triton_per_fused_mean_2 = async_compile.triton('triton_per_fused_mean_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, 4],
    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_mean_2', 'mutated_arg_names': ['in_out_ptr0'], '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_mean_2(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 3
    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 = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 3.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp6, 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, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg0_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf11 = empty_strided_cuda((3, ), (1, ), torch.float32)
        buf10 = reinterpret_tensor(buf11, (1, ), (1, ), 2)  # alias
        buf9 = reinterpret_tensor(buf11, (1, ), (1, ), 1)  # alias
        buf8 = reinterpret_tensor(buf11, (1, ), (1, ), 0)  # alias
        # Topologically Sorted Source Nodes: [softmax, prediction, eq, eq_1, add, eq_2, float_1, sum_1, eq_3, float_2, sum_2, eq_4, eq_5, add_3, eq_6, float_3, sum_3, eq_7, float_4, sum_4, eq_8, eq_9, add_6, eq_10, float_5, sum_5, eq_11, float_6, sum_6, accs], Original ATen: [aten._softmax, aten.argmax, aten.eq, aten.add, aten._to_copy, aten.sum, aten.stack]
        triton_per_fused__softmax__to_copy_add_argmax_eq_stack_sum_1.run(buf0, arg1_1, buf10, buf9, buf8, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
        buf12 = empty_strided_cuda((), (), torch.float32)
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [mean], Original ATen: [aten.mean]
        triton_per_fused_mean_2.run(buf13, buf11, 1, 3, grid=grid(1), stream=stream0)
        del buf10
        del buf11
        del buf8
        del buf9
    return (buf13, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    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.functional
import torch.nn as nn
import torch.nn.functional as F


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class Accuracy(nn.Module):

    def __init__(self, bback_ignore=True):
        super(Accuracy, self).__init__()
        self.bback_ignore = bback_ignore

    def forward(self, y_pred, y_true):
        _n, ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        prob = F.softmax(y_pred, dim=1).data
        prediction = torch.argmax(prob, 1)
        accs = []
        for c in range(int(self.bback_ignore), ch):
            yt_c = y_true[:, c, ...]
            num = (prediction.eq(c) + yt_c.data.eq(1)).eq(2).float().sum() + 1
            den = yt_c.data.eq(1).float().sum() + 1
            acc = num / den * 100
            accs.append(acc)
        accs = torch.stack(accs)
        return accs.mean()


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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
import torch.nn.functional
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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_per_fused__softmax__to_copy_add_argmax_eq_stack_sum_1(in_ptr0,
    in_ptr1, out_ptr7, out_ptr8, out_ptr9, 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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp55 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp66 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp76 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1, 1], 0, tl.int64)
    tmp18 = tl.full([1, 1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1, 1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1, 1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tmp54 = tmp53 == tmp18
    tmp56 = 1.0
    tmp57 = tmp55 == tmp56
    tmp58 = tmp54 | tmp57
    tmp59 = tmp58.to(tl.int64)
    tmp60 = tmp59 == tmp33
    tmp61 = tmp60.to(tl.float32)
    tmp62 = tl.broadcast_to(tmp61, [XBLOCK, RBLOCK])
    tmp64 = tl.sum(tmp62, 1)[:, None]
    tmp65 = tmp53 == tmp33
    tmp67 = tmp66 == tmp56
    tmp68 = tmp65 | tmp67
    tmp69 = tmp68.to(tl.int64)
    tmp70 = tmp69 == tmp33
    tmp71 = tmp70.to(tl.float32)
    tmp72 = tl.broadcast_to(tmp71, [XBLOCK, RBLOCK])
    tmp74 = tl.sum(tmp72, 1)[:, None]
    tmp75 = tmp53 == tmp48
    tmp77 = tmp76 == tmp56
    tmp78 = tmp75 | tmp77
    tmp79 = tmp78.to(tl.int64)
    tmp80 = tmp79 == tmp33
    tmp81 = tmp80.to(tl.float32)
    tmp82 = tl.broadcast_to(tmp81, [XBLOCK, RBLOCK])
    tmp84 = tl.sum(tmp82, 1)[:, None]
    tmp85 = tmp57.to(tl.float32)
    tmp86 = tl.broadcast_to(tmp85, [XBLOCK, RBLOCK])
    tmp88 = tl.sum(tmp86, 1)[:, None]
    tmp89 = tmp67.to(tl.float32)
    tmp90 = tl.broadcast_to(tmp89, [XBLOCK, RBLOCK])
    tmp92 = tl.sum(tmp90, 1)[:, None]
    tmp93 = tmp77.to(tl.float32)
    tmp94 = tl.broadcast_to(tmp93, [XBLOCK, RBLOCK])
    tmp96 = tl.sum(tmp94, 1)[:, None]
    tmp97 = tmp84 + tmp56
    tmp98 = tmp96 + tmp56
    tmp99 = tmp97 / tmp98
    tmp100 = 100.0
    tmp101 = tmp99 * tmp100
    tmp102 = tmp74 + tmp56
    tmp103 = tmp92 + tmp56
    tmp104 = tmp102 / tmp103
    tmp105 = tmp104 * tmp100
    tmp106 = tmp64 + tmp56
    tmp107 = tmp88 + tmp56
    tmp108 = tmp106 / tmp107
    tmp109 = tmp108 * tmp100
    tl.store(out_ptr7 + tl.full([XBLOCK, 1], 0, tl.int32), tmp101, None)
    tl.store(out_ptr8 + tl.full([XBLOCK, 1], 0, tl.int32), tmp105, None)
    tl.store(out_ptr9 + tl.full([XBLOCK, 1], 0, tl.int32), tmp109, None)


@triton.jit
def triton_per_fused_mean_2(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    rnumel = 3
    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, :]
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, rmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(rmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 3.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
        buf11 = empty_strided_cuda((3,), (1,), torch.float32)
        buf10 = reinterpret_tensor(buf11, (1,), (1,), 2)
        buf9 = reinterpret_tensor(buf11, (1,), (1,), 1)
        buf8 = reinterpret_tensor(buf11, (1,), (1,), 0)
        triton_per_fused__softmax__to_copy_add_argmax_eq_stack_sum_1[grid(1)](
            buf0, arg1_1, buf10, buf9, buf8, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del buf0
        buf12 = empty_strided_cuda((), (), torch.float32)
        buf13 = buf12
        del buf12
        triton_per_fused_mean_2[grid(1)](buf13, buf11, 1, 3, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf10
        del buf11
        del buf8
        del buf9
    return buf13,


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class AccuracyNew(nn.Module):

    def __init__(self, bback_ignore=True):
        super(AccuracyNew, self).__init__()
        self.bback_ignore = bback_ignore

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