# 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_0/inductor_cache/wp/cwp4ssoyg4ryz2y6xj5oqcpwqbhw5pjsafwlwacoijsmc4meri3d.py
# Topologically Sorted Source Nodes: [add, log, b, sum_1, b_1, b_2], Original ATen: [aten.add, aten.log, aten.mul, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   b => mul
#   b_1 => mul_1
#   b_2 => mean
#   log => log
#   sum_1 => sum_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view, 1e-12), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %log), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, -1.0), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_1,), kwargs = {})
triton_per_fused_add_log_mean_mul_sum_0 = async_compile.triton('triton_per_fused_add_log_mean_mul_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.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_add_log_mean_mul_sum_0', 'mutated_arg_names': ['in_out_ptr0'], '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_add_log_mean_mul_sum_0(in_out_ptr0, in_ptr0, 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 + ((64*(r0 // 16)) + (r0 % 16)), None)
    tmp5 = tl.load(in_ptr0 + (16 + (64*(r0 // 16)) + (r0 % 16)), None)
    tmp10 = tl.load(in_ptr0 + (32 + (64*(r0 // 16)) + (r0 % 16)), None)
    tmp15 = tl.load(in_ptr0 + (48 + (64*(r0 // 16)) + (r0 % 16)), None)
    tmp1 = 1e-12
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp0 * tmp3
    tmp6 = tmp5 + tmp1
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tmp4 + tmp8
    tmp11 = tmp10 + tmp1
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp10 * tmp12
    tmp14 = tmp9 + tmp13
    tmp16 = tmp15 + tmp1
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp15 * tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = -1.0
    tmp21 = tmp19 * tmp20
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = 64.0
    tmp26 = tmp24 / tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp26, 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((), (), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [add, log, b, sum_1, b_1, b_2], Original ATen: [aten.add, aten.log, aten.mul, aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_sum_0.run(buf1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf1, )


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 triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
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_add_log_mean_mul_sum_0(in_out_ptr0, in_ptr0, 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 + (64 * (r0 // 16) + r0 % 16), None)
    tmp5 = tl.load(in_ptr0 + (16 + 64 * (r0 // 16) + r0 % 16), None)
    tmp10 = tl.load(in_ptr0 + (32 + 64 * (r0 // 16) + r0 % 16), None)
    tmp15 = tl.load(in_ptr0 + (48 + 64 * (r0 // 16) + r0 % 16), None)
    tmp1 = 1e-12
    tmp2 = tmp0 + tmp1
    tmp3 = tl_math.log(tmp2)
    tmp4 = tmp0 * tmp3
    tmp6 = tmp5 + tmp1
    tmp7 = tl_math.log(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tmp4 + tmp8
    tmp11 = tmp10 + tmp1
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp10 * tmp12
    tmp14 = tmp9 + tmp13
    tmp16 = tmp15 + tmp1
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp15 * tmp17
    tmp19 = tmp14 + tmp18
    tmp20 = -1.0
    tmp21 = tmp19 * tmp20
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = 64.0
    tmp26 = tmp24 / tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, 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((), (), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_log_mean_mul_sum_0[grid(1)](buf1, arg0_1, 1, 
            64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


def feature_map_permute(input):
    s = input.data.shape
    l = len(s)
    if l == 2:
        x = input
    elif l == 3:
        x = input.permute(0, 2, 1)
    elif l == 4:
        x = input.permute(0, 2, 3, 1)
    elif l == 5:
        x = input.permute(0, 2, 3, 4, 1)
    else:
        x = []
        None
    x = x.contiguous()
    x = x.view(-1, s[1])
    return x


class EntropyLoss(nn.Module):

    def __init__(self, eps=1e-12):
        super(EntropyLoss, self).__init__()
        self.eps = eps

    def forward(self, x):
        b = x * torch.log(x + self.eps)
        b = -1.0 * b.sum(dim=1)
        b = b.mean()
        return b


class EntropyLossEncapNew(nn.Module):

    def __init__(self, eps=1e-12):
        super(EntropyLossEncapNew, self).__init__()
        self.eps = eps
        self.entropy_loss = EntropyLoss(eps)

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