# 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/fw/cfwkei3isw2pa2xamwapcpaqiplvhdz4zywym425thozidzx67qh.py
# Topologically Sorted Source Nodes: [logits], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   logits => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg1_1, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg1_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=[16384], 
    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 = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (4*x1), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), None, 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 + (x2), tmp9, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6x/c6xwmwqiowkgjifetryiou7grm3j3yh47xzqxfhbfn6iod2x53ci.py
# Topologically Sorted Source Nodes: [logits], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   logits => div, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[16384], 
    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_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__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr0 + (4*x1), None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hn/chny2er4wobzy5w7o5w6rfshqgzq6kjifapigwwqu5pzmqi464o4.py
# Topologically Sorted Source Nodes: [idx, one_hot_key_1, mul, sub, pow_1, mul_1, add, log, loss, loss_1, mean], Original ATen: [aten._to_copy, aten.scatter, aten.mul, aten.rsub, aten.pow, aten.add, aten.log, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   idx => convert_element_type
#   log => log
#   loss => mul_2
#   loss_1 => sum_2
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   one_hot_key_1 => scatter_upon_const_tensor
#   pow_1 => pow_1
#   sub => sub_1
# Graph fragment:
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%view, torch.int64), kwargs = {})
#   %scatter_upon_const_tensor : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.scatter_upon_const_tensor](args = (), kwargs = {shape: [256, 4], background_val: 0, dtype: torch.float32, dim: 1, selector: %convert_element_type, val: 1})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%scatter_upon_const_tensor, -0.25), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %pow_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 1e-09), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %log), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_2,), kwargs = {})
triton_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_sum_2 = async_compile.triton('triton_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_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.reduction(
    size_hints=[1, 4096],
    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_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, '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_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_sum_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp42 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = (rindex // 4) % 256
        r0 = rindex % 4
        r2 = (rindex // 1024)
        r4 = rindex % 1024
        r3 = rindex
        tmp0 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0)
        tmp9 = tl.load(in_ptr1 + (r4 + (4096*r2)), rmask, eviction_policy='evict_first', other=0.0)
        tmp17 = tl.load(in_ptr1 + (1024 + r4 + (4096*r2)), rmask, eviction_policy='evict_first', other=0.0)
        tmp25 = tl.load(in_ptr1 + (2048 + r4 + (4096*r2)), rmask, eviction_policy='evict_first', other=0.0)
        tmp33 = tl.load(in_ptr1 + (3072 + r4 + (4096*r2)), rmask, eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0.to(tl.int64)
        tmp2 = r0
        tmp3 = tmp1 == tmp2
        tmp4 = 1.0
        tmp5 = 0.0
        tmp6 = tl.where(tmp3, tmp4, tmp5)
        tmp7 = -0.25
        tmp8 = tmp6 * tmp7
        tmp10 = tmp4 - tmp9
        tmp11 = tmp10 * tmp10
        tmp12 = tmp8 * tmp11
        tmp13 = 1e-09
        tmp14 = tmp9 + tmp13
        tmp15 = tl_math.log(tmp14)
        tmp16 = tmp12 * tmp15
        tmp18 = tmp4 - tmp17
        tmp19 = tmp18 * tmp18
        tmp20 = tmp8 * tmp19
        tmp21 = tmp17 + tmp13
        tmp22 = tl_math.log(tmp21)
        tmp23 = tmp20 * tmp22
        tmp24 = tmp16 + tmp23
        tmp26 = tmp4 - tmp25
        tmp27 = tmp26 * tmp26
        tmp28 = tmp8 * tmp27
        tmp29 = tmp25 + tmp13
        tmp30 = tl_math.log(tmp29)
        tmp31 = tmp28 * tmp30
        tmp32 = tmp24 + tmp31
        tmp34 = tmp4 - tmp33
        tmp35 = tmp34 * tmp34
        tmp36 = tmp8 * tmp35
        tmp37 = tmp33 + tmp13
        tmp38 = tl_math.log(tmp37)
        tmp39 = tmp36 * tmp38
        tmp40 = tmp32 + tmp39
        tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
        tmp43 = _tmp42 + tmp41
        _tmp42 = tl.where(rmask, tmp43, _tmp42)
    tmp42 = tl.sum(_tmp42, 1)[:, None]
    tmp44 = 4096.0
    tmp45 = tmp42 / tmp44
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp45, 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, 256, 4), (4096, 1024, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 256, 4), (4096, 1024, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg1_1, buf0, 16384, grid=grid(16384), stream=stream0)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 256, 4), (4096, 1024, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf0, buf1, 16384, grid=grid(16384), stream=stream0)
        del buf0
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [idx, one_hot_key_1, mul, sub, pow_1, mul_1, add, log, loss, loss_1, mean], Original ATen: [aten._to_copy, aten.scatter, aten.mul, aten.rsub, aten.pow, aten.add, aten.log, aten.sum, aten.mean]
        triton_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_sum_2.run(buf4, arg0_1, buf1, 1, 4096, grid=grid(1), stream=stream0)
        del arg0_1
        del buf1
    return (buf4, )


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, 256, 4), (4096, 1024, 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 as nn
import torch.nn.functional as F


class FocalLoss(nn.Module):
    """
    Softmax and sigmoid focal loss
    """

    def __init__(self, num_labels, activation_type='softmax', gamma=2.0,
        alpha=0.25, epsilon=1e-09):
        super(FocalLoss, self).__init__()
        self.num_labels = num_labels
        self.gamma = gamma
        self.alpha = alpha
        self.epsilon = epsilon
        self.activation_type = activation_type

    def forward(self, input, target):
        """
        Args:
            logits: model's output, shape of [batch_size, num_cls]
            target: ground truth labels, shape of [batch_size]
        Returns:
            shape of [batch_size]
        """
        if self.activation_type == 'softmax':
            idx = target.view(-1, 1).long()
            one_hot_key = torch.zeros(idx.size(0), self.num_labels, dtype=
                torch.float32, device=idx.device)
            one_hot_key = one_hot_key.scatter_(1, idx, 1)
            logits = F.softmax(input, dim=-1)
            loss = -self.alpha * one_hot_key * torch.pow(1 - logits, self.gamma
                ) * (logits + self.epsilon).log()
            loss = loss.sum(1)
        elif self.activation_type == 'sigmoid':
            multi_hot_key = target
            logits = F.sigmoid(input)
            zero_hot_key = 1 - multi_hot_key
            loss = -self.alpha * multi_hot_key * torch.pow(1 - logits, self
                .gamma) * (logits + self.epsilon).log()
            loss += -(1 - self.alpha) * zero_hot_key * torch.pow(logits,
                self.gamma) * (1 - logits + self.epsilon).log()
        return loss.mean()


def get_inputs():
    return [torch.rand([4, 4, 256, 4]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'num_labels': 4}]

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_poi_fused__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + 4 * x1, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), None, 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 + x2, tmp9, None)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + 4 * x1, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, None)


@triton.jit
def triton_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_sum_2(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    rnumel = 4096
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp42 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex // 4 % 256
        r0 = rindex % 4
        r2 = rindex // 1024
        r4 = rindex % 1024
        tmp0 = tl.load(in_ptr0 + r1, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp9 = tl.load(in_ptr1 + (r4 + 4096 * r2), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp17 = tl.load(in_ptr1 + (1024 + r4 + 4096 * r2), rmask,
            eviction_policy='evict_first', other=0.0)
        tmp25 = tl.load(in_ptr1 + (2048 + r4 + 4096 * r2), rmask,
            eviction_policy='evict_first', other=0.0)
        tmp33 = tl.load(in_ptr1 + (3072 + r4 + 4096 * r2), rmask,
            eviction_policy='evict_first', other=0.0)
        tmp1 = tmp0.to(tl.int64)
        tmp2 = r0
        tmp3 = tmp1 == tmp2
        tmp4 = 1.0
        tmp5 = 0.0
        tmp6 = tl.where(tmp3, tmp4, tmp5)
        tmp7 = -0.25
        tmp8 = tmp6 * tmp7
        tmp10 = tmp4 - tmp9
        tmp11 = tmp10 * tmp10
        tmp12 = tmp8 * tmp11
        tmp13 = 1e-09
        tmp14 = tmp9 + tmp13
        tmp15 = tl_math.log(tmp14)
        tmp16 = tmp12 * tmp15
        tmp18 = tmp4 - tmp17
        tmp19 = tmp18 * tmp18
        tmp20 = tmp8 * tmp19
        tmp21 = tmp17 + tmp13
        tmp22 = tl_math.log(tmp21)
        tmp23 = tmp20 * tmp22
        tmp24 = tmp16 + tmp23
        tmp26 = tmp4 - tmp25
        tmp27 = tmp26 * tmp26
        tmp28 = tmp8 * tmp27
        tmp29 = tmp25 + tmp13
        tmp30 = tl_math.log(tmp29)
        tmp31 = tmp28 * tmp30
        tmp32 = tmp24 + tmp31
        tmp34 = tmp4 - tmp33
        tmp35 = tmp34 * tmp34
        tmp36 = tmp8 * tmp35
        tmp37 = tmp33 + tmp13
        tmp38 = tl_math.log(tmp37)
        tmp39 = tmp36 * tmp38
        tmp40 = tmp32 + tmp39
        tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
        tmp43 = _tmp42 + tmp41
        _tmp42 = tl.where(rmask, tmp43, _tmp42)
    tmp42 = tl.sum(_tmp42, 1)[:, None]
    tmp44 = 4096.0
    tmp45 = tmp42 / tmp44
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp45, 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, 256, 4), (4096, 1024, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 256, 4), (4096, 1024, 4, 1), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16384)](arg1_1, buf0, 16384,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 256, 4), (4096, 1024, 4, 1), torch
            .float32)
        triton_poi_fused__softmax_1[grid(16384)](buf0, buf1, 16384, XBLOCK=
            256, num_warps=4, num_stages=1)
        del buf0
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_red_fused__to_copy_add_log_mean_mul_pow_rsub_scatter_sum_2[grid
            (1)](buf4, arg0_1, buf1, 1, 4096, XBLOCK=1, RBLOCK=2048,
            num_warps=16, num_stages=1)
        del arg0_1
        del buf1
    return buf4,


class FocalLossNew(nn.Module):
    """
    Softmax and sigmoid focal loss
    """

    def __init__(self, num_labels, activation_type='softmax', gamma=2.0,
        alpha=0.25, epsilon=1e-09):
        super(FocalLossNew, self).__init__()
        self.num_labels = num_labels
        self.gamma = gamma
        self.alpha = alpha
        self.epsilon = epsilon
        self.activation_type = activation_type

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