# 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/rl/crlsa6m6cvg2mlnuup5lug7qmy6ktr6vdzefb7guym4fkd3sca4j.py
# Topologically Sorted Source Nodes: [mul_1, sub, sub_1, mul_2, focal_factor, sub_2, pow_1, clamp, log, mul, sum_1, cross_entropy_loss, mul_3, mul_4, sum_2, loss], Original ATen: [aten.mul, aten.rsub, aten.add, aten.pow, aten.clamp, aten.log, aten.sum, aten.neg, aten.mean]
# Source node to ATen node mapping:
#   clamp => clamp_min
#   cross_entropy_loss => neg
#   focal_factor => add
#   log => log
#   loss => mean
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   pow_1 => pow_1
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg0_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg1_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %sub_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %add), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2.0), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg0_1, 1e-12), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%clamp_min,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %log), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [0]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %neg), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, 0.25), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_4, [0]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_per_fused_add_clamp_log_mean_mul_neg_pow_rsub_sum_0 = async_compile.triton('triton_per_fused_add_clamp_log_mean_mul_neg_pow_rsub_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: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_clamp_log_mean_mul_neg_pow_rsub_sum_0', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 8, '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_clamp_log_mean_mul_neg_pow_rsub_sum_0(in_out_ptr1, in_ptr0, in_ptr1, 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 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp6 = tl.load(in_ptr0 + (64 + r0), None)
    tmp7 = tl.load(in_ptr1 + (64 + r0), None)
    tmp12 = tl.load(in_ptr0 + (128 + r0), None)
    tmp13 = tl.load(in_ptr1 + (128 + r0), None)
    tmp18 = tl.load(in_ptr0 + (192 + r0), None)
    tmp19 = tl.load(in_ptr1 + (192 + r0), None)
    tmp2 = 1e-12
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tl_math.log(tmp3)
    tmp5 = tmp0 * tmp4
    tmp8 = triton_helpers.maximum(tmp7, tmp2)
    tmp9 = tl_math.log(tmp8)
    tmp10 = tmp6 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = triton_helpers.maximum(tmp13, tmp2)
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp12 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = triton_helpers.maximum(tmp19, tmp2)
    tmp21 = tl_math.log(tmp20)
    tmp22 = tmp18 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = -tmp23
    tmp25 = tmp1 * tmp0
    tmp26 = 1.0
    tmp27 = tmp26 - tmp1
    tmp28 = tmp26 - tmp0
    tmp29 = tmp27 * tmp28
    tmp30 = tmp25 + tmp29
    tmp31 = tmp26 - tmp30
    tmp32 = tmp31 * tmp31
    tmp33 = tmp32 * tmp24
    tmp34 = 0.25
    tmp35 = tmp33 * tmp34
    tmp36 = tmp7 * tmp6
    tmp37 = tmp26 - tmp7
    tmp38 = tmp26 - tmp6
    tmp39 = tmp37 * tmp38
    tmp40 = tmp36 + tmp39
    tmp41 = tmp26 - tmp40
    tmp42 = tmp41 * tmp41
    tmp43 = tmp42 * tmp24
    tmp44 = tmp43 * tmp34
    tmp45 = tmp35 + tmp44
    tmp46 = tmp13 * tmp12
    tmp47 = tmp26 - tmp13
    tmp48 = tmp26 - tmp12
    tmp49 = tmp47 * tmp48
    tmp50 = tmp46 + tmp49
    tmp51 = tmp26 - tmp50
    tmp52 = tmp51 * tmp51
    tmp53 = tmp52 * tmp24
    tmp54 = tmp53 * tmp34
    tmp55 = tmp45 + tmp54
    tmp56 = tmp19 * tmp18
    tmp57 = tmp26 - tmp19
    tmp58 = tmp26 - tmp18
    tmp59 = tmp57 * tmp58
    tmp60 = tmp56 + tmp59
    tmp61 = tmp26 - tmp60
    tmp62 = tmp61 * tmp61
    tmp63 = tmp62 * tmp24
    tmp64 = tmp63 * tmp34
    tmp65 = tmp55 + tmp64
    tmp66 = tl.broadcast_to(tmp65, [XBLOCK, RBLOCK])
    tmp68 = tl.sum(tmp66, 1)[:, None]
    tmp69 = 64.0
    tmp70 = tmp68 / tmp69
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp70, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [mul_1, sub, sub_1, mul_2, focal_factor, sub_2, pow_1, clamp, log, mul, sum_1, cross_entropy_loss, mul_3, mul_4, sum_2, loss], Original ATen: [aten.mul, aten.rsub, aten.add, aten.pow, aten.clamp, aten.log, aten.sum, aten.neg, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_log_mean_mul_neg_pow_rsub_sum_0.run(buf3, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    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)
    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 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 as 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_clamp_log_mean_mul_neg_pow_rsub_sum_0(in_out_ptr1,
    in_ptr0, in_ptr1, 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 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp6 = tl.load(in_ptr0 + (64 + r0), None)
    tmp7 = tl.load(in_ptr1 + (64 + r0), None)
    tmp12 = tl.load(in_ptr0 + (128 + r0), None)
    tmp13 = tl.load(in_ptr1 + (128 + r0), None)
    tmp18 = tl.load(in_ptr0 + (192 + r0), None)
    tmp19 = tl.load(in_ptr1 + (192 + r0), None)
    tmp2 = 1e-12
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tl_math.log(tmp3)
    tmp5 = tmp0 * tmp4
    tmp8 = triton_helpers.maximum(tmp7, tmp2)
    tmp9 = tl_math.log(tmp8)
    tmp10 = tmp6 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = triton_helpers.maximum(tmp13, tmp2)
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp12 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = triton_helpers.maximum(tmp19, tmp2)
    tmp21 = tl_math.log(tmp20)
    tmp22 = tmp18 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = -tmp23
    tmp25 = tmp1 * tmp0
    tmp26 = 1.0
    tmp27 = tmp26 - tmp1
    tmp28 = tmp26 - tmp0
    tmp29 = tmp27 * tmp28
    tmp30 = tmp25 + tmp29
    tmp31 = tmp26 - tmp30
    tmp32 = tmp31 * tmp31
    tmp33 = tmp32 * tmp24
    tmp34 = 0.25
    tmp35 = tmp33 * tmp34
    tmp36 = tmp7 * tmp6
    tmp37 = tmp26 - tmp7
    tmp38 = tmp26 - tmp6
    tmp39 = tmp37 * tmp38
    tmp40 = tmp36 + tmp39
    tmp41 = tmp26 - tmp40
    tmp42 = tmp41 * tmp41
    tmp43 = tmp42 * tmp24
    tmp44 = tmp43 * tmp34
    tmp45 = tmp35 + tmp44
    tmp46 = tmp13 * tmp12
    tmp47 = tmp26 - tmp13
    tmp48 = tmp26 - tmp12
    tmp49 = tmp47 * tmp48
    tmp50 = tmp46 + tmp49
    tmp51 = tmp26 - tmp50
    tmp52 = tmp51 * tmp51
    tmp53 = tmp52 * tmp24
    tmp54 = tmp53 * tmp34
    tmp55 = tmp45 + tmp54
    tmp56 = tmp19 * tmp18
    tmp57 = tmp26 - tmp19
    tmp58 = tmp26 - tmp18
    tmp59 = tmp57 * tmp58
    tmp60 = tmp56 + tmp59
    tmp61 = tmp26 - tmp60
    tmp62 = tmp61 * tmp61
    tmp63 = tmp62 * tmp24
    tmp64 = tmp63 * tmp34
    tmp65 = tmp55 + tmp64
    tmp66 = tl.broadcast_to(tmp65, [XBLOCK, RBLOCK])
    tmp68 = tl.sum(tmp66, 1)[:, None]
    tmp69 = 64.0
    tmp70 = tmp68 / tmp69
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp70, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        get_raw_stream(0)
        triton_per_fused_add_clamp_log_mean_mul_neg_pow_rsub_sum_0[grid(1)](
            buf3, arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf3,


class FocalLossMultiClassNew(nn.Module):
    """
    Implementation of the multi class focal loss.
    https://arxiv.org/abs/1708.02002
    """

    def __init__(self, alpha: 'float'=0.25, gamma: 'float'=2.0) ->None:
        """
        Constructor method
        :param alpha: (float) Alpha constant
        :param gamma: (float) Gamma constant (see paper)
        """
        super(FocalLossMultiClassNew, self).__init__()
        self.alpha = alpha
        self.gamma = gamma

    def __repr__(self):
        """
        Get representation of the loss module
        :return: (str) String including information
        """
        return '{}, alpha={}, gamma={}'.format(self.__class__.__name__,
            self.alpha, self.gamma)

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