# 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_8/inductor_cache/as/caskboixxolni2tppc7ebhvxctflv3fb7aor6x7azuyniao6ssw7.py
# Topologically Sorted Source Nodes: [max_1, eq, eq_1, or_, sub, exp, sum_1, log, add, x, sub_1, mul, expm1], Original ATen: [aten.max, aten.eq, aten.bitwise_or, aten.sub, aten.exp, aten.sum, aten.log, aten.add, aten.where, aten.mul, aten.expm1]
# Source node to ATen node mapping:
#   add => add
#   eq => eq
#   eq_1 => eq_1
#   exp => exp
#   expm1 => expm1
#   log => log
#   max_1 => max_1
#   mul => mul
#   or_ => bitwise_or
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   x => where
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%addmm, -1, True), kwargs = {})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%getitem, inf), kwargs = {})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%getitem, -inf), kwargs = {})
#   %bitwise_or : [num_users=1] = call_function[target=torch.ops.aten.bitwise_or.Tensor](args = (%eq, %eq_1), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, %log), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%bitwise_or, %getitem, %add), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %where), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, -3.0), kwargs = {})
#   %expm1 : [num_users=1] = call_function[target=torch.ops.aten.expm1.default](args = (%mul,), kwargs = {})
triton_poi_fused_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_0 = async_compile.triton('triton_poi_fused_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_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=[16], 
    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_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_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_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = float("inf")
    tmp9 = tmp7 == tmp8
    tmp10 = float("-inf")
    tmp11 = tmp7 == tmp10
    tmp12 = tmp9 | tmp11
    tmp13 = tmp1 - tmp7
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp2 - tmp7
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp4 - tmp7
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp6 - tmp7
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tl_math.log(tmp23)
    tmp25 = tmp7 + tmp24
    tmp26 = tl.where(tmp12, tmp7, tmp25)
    tmp27 = tmp0 - tmp26
    tmp28 = -3.0
    tmp29 = tmp27 * tmp28
    tmp30 = libdevice.expm1(tmp29)
    tl.store(out_ptr0 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/mg/cmgmnndpqlm6qlhb2u6fmu5bg3b37xycyy4d6aq3vsh3odgpzagm.py
# Topologically Sorted Source Nodes: [softmaxed_all_head_res, gather, sum_2, total_loss, truediv_1], Original ATen: [aten.div, aten.gather, aten.sum, aten.neg]
# Source node to ATen node mapping:
#   gather => gather
#   softmaxed_all_head_res => div
#   sum_2 => sum_2
#   total_loss => neg
#   truediv_1 => div_1
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expm1, -12.0), kwargs = {})
#   %gather : [num_users=1] = call_function[target=torch.ops.aten.gather.default](args = (%div, 1, %view), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%gather,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_2,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, 4), kwargs = {})
triton_per_fused_div_gather_neg_sum_1 = async_compile.triton('triton_per_fused_div_gather_neg_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, 4],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_gather_neg_sum_1', '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_div_gather_neg_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    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 = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (tmp4 + (4*r0)), None, eviction_policy='evict_last')
    tmp7 = -0.08333333333333333
    tmp8 = tmp6 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = -tmp11
    tmp13 = 0.25
    tmp14 = tmp12 * tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp14, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [all_head_res], Original ATen: [aten.addmm]
        extern_kernels.addmm(arg1_1, arg0_1, reinterpret_tensor(arg2_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_1, eq, eq_1, or_, sub, exp, sum_1, log, add, x, sub_1, mul, expm1], Original ATen: [aten.max, aten.eq, aten.bitwise_or, aten.sub, aten.exp, aten.sum, aten.log, aten.add, aten.where, aten.mul, aten.expm1]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_0.run(buf0, buf1, 16, grid=grid(16), stream=stream0)
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [softmaxed_all_head_res, gather, sum_2, total_loss, truediv_1], Original ATen: [aten.div, aten.gather, aten.sum, aten.neg]
        triton_per_fused_div_gather_neg_sum_1.run(buf3, arg3_1, buf1, 1, 4, grid=grid(1), stream=stream0)
        del arg3_1
        del buf1
    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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.int64)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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


def logsumexp(x, dim=None, keepdim=False):
    if dim is None:
        x, dim = x.view(-1), 0
    xm, _ = torch.max(x, dim, keepdim=True)
    x = torch.where((xm == float('inf')) | (xm == float('-inf')), xm, xm +
        torch.log(torch.sum(torch.exp(x - xm), dim, keepdim=True)))
    return x if keepdim else x.squeeze(dim)


class SplitCrossEntropyLoss(nn.Module):
    """SplitCrossEntropyLoss calculates an approximate softmax"""

    def __init__(self, hidden_size, q, b, g):
        super(SplitCrossEntropyLoss, self).__init__()
        self.hidden_size = hidden_size
        self.q = q
        self.b = b
        self.g = g

    def forward(self, weight, bias, hiddens, targets, training=True):
        total_loss = None
        if len(hiddens.size()) > 2:
            hiddens = hiddens.view(-1, hiddens.size(2))
        all_head_res = torch.nn.functional.linear(hiddens, weight, bias=bias)
        if not self.q == 1.0 and training:
            softmaxed_all_head_res, _sum_res = self.log_q(all_head_res)
        elif not self.b == 1.0 and training:
            softmaxed_all_head_res, _sum_res = self.log_b(all_head_res)
        elif not self.g == 1.0 and training:
            softmaxed_all_head_res, _sum_res = self.log_g(all_head_res)
        else:
            softmaxed_all_head_res = torch.nn.functional.log_softmax(
                all_head_res, dim=-1)
        total_loss = -torch.gather(softmaxed_all_head_res, dim=1, index=
            targets.view(-1, 1)).float().sum()
        return (total_loss / len(targets)).type_as(weight)

    def log_b(self, logits):
        lnorm = logsumexp(logits, dim=-1, keepdim=True)
        lsum = torch.exp(self.b * logits).sum(dim=1, keepdim=True) * torch.exp(
            -self.b * lnorm)
        return torch.exp((self.b - 1.0) * (logits - lnorm)) / (self.b - 1.0
            ) - lsum / self.b - 1.0 / ((self.b - 1.0) * self.b), lsum

    def log_g(self, logits):
        lnorm = logsumexp(logits, dim=-1, keepdim=True)
        lsum = torch.exp(self.g * logits).sum(dim=1, keepdim=True)
        return logits - torch.log(lsum) / self.g, lsum * torch.exp(-self.g *
            lnorm)

    def log_q(self, logits):
        lnorm = logsumexp(logits, dim=-1, keepdim=True)
        return torch.expm1((1.0 - self.q) * (logits - lnorm)) / ((1.0 -
            self.q) * self.q), torch.exp(lnorm)


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([4, 4]), torch.rand([4, 4]),
        torch.ones([4], dtype=torch.int64)]


def get_init_inputs():
    return [[], {'hidden_size': 4, 'q': 4, 'b': 4, 'g': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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 libdevice, 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_0(
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = float('inf')
    tmp9 = tmp7 == tmp8
    tmp10 = float('-inf')
    tmp11 = tmp7 == tmp10
    tmp12 = tmp9 | tmp11
    tmp13 = tmp1 - tmp7
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp2 - tmp7
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp18 = tmp4 - tmp7
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp6 - tmp7
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tl_math.log(tmp23)
    tmp25 = tmp7 + tmp24
    tmp26 = tl.where(tmp12, tmp7, tmp25)
    tmp27 = tmp0 - tmp26
    tmp28 = -3.0
    tmp29 = tmp27 * tmp28
    tmp30 = libdevice.expm1(tmp29)
    tl.store(out_ptr0 + x2, tmp30, xmask)


@triton.jit
def triton_per_fused_div_gather_neg_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4),
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp7 = -0.08333333333333333
    tmp8 = tmp6 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.sum(tmp9, 1)[:, None]
    tmp12 = -tmp11
    tmp13 = 0.25
    tmp14 = tmp12 * tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp14, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    assert_size_stride(arg3_1, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(arg1_1, arg0_1, reinterpret_tensor(arg2_1, (4,
            4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_bitwise_or_eq_exp_expm1_log_max_mul_sub_sum_where_0[
            grid(16)](buf0, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_div_gather_neg_sum_1[grid(1)](buf3, arg3_1, buf1, 
            1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg3_1
        del buf1
    return buf3,


def logsumexp(x, dim=None, keepdim=False):
    if dim is None:
        x, dim = x.view(-1), 0
    xm, _ = torch.max(x, dim, keepdim=True)
    x = torch.where((xm == float('inf')) | (xm == float('-inf')), xm, xm +
        torch.log(torch.sum(torch.exp(x - xm), dim, keepdim=True)))
    return x if keepdim else x.squeeze(dim)


class SplitCrossEntropyLossNew(nn.Module):
    """SplitCrossEntropyLoss calculates an approximate softmax"""

    def __init__(self, hidden_size, q, b, g):
        super(SplitCrossEntropyLossNew, self).__init__()
        self.hidden_size = hidden_size
        self.q = q
        self.b = b
        self.g = g

    def log_b(self, logits):
        lnorm = logsumexp(logits, dim=-1, keepdim=True)
        lsum = torch.exp(self.b * logits).sum(dim=1, keepdim=True) * torch.exp(
            -self.b * lnorm)
        return torch.exp((self.b - 1.0) * (logits - lnorm)) / (self.b - 1.0
            ) - lsum / self.b - 1.0 / ((self.b - 1.0) * self.b), lsum

    def log_g(self, logits):
        lnorm = logsumexp(logits, dim=-1, keepdim=True)
        lsum = torch.exp(self.g * logits).sum(dim=1, keepdim=True)
        return logits - torch.log(lsum) / self.g, lsum * torch.exp(-self.g *
            lnorm)

    def log_q(self, logits):
        lnorm = logsumexp(logits, dim=-1, keepdim=True)
        return torch.expm1((1.0 - self.q) * (logits - lnorm)) / ((1.0 -
            self.q) * self.q), torch.exp(lnorm)

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]
