# 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_9/inductor_cache/ix/cixrcqjs3mmxfrmb4ar6p2ap7t2kmg24knop5yns67fddlpjwuz7.py
# Topologically Sorted Source Nodes: [eq, float_1, neg, sub, pow_1, add, log, term1, mul_2, mul_3, ne, ge, mul_4, float_2, pow_2, sub_1, add_1, log_1, term2, mul_5, mul_6, loss, sum_1, truediv], Original ATen: [aten.eq, aten._to_copy, aten.neg, aten.rsub, aten.pow, aten.add, aten.log, aten.mul, aten.ne, aten.ge, aten.sub, aten.sum, aten.div]
# Source node to ATen node mapping:
#   add => add_1
#   add_1 => add_2
#   eq => eq
#   float_1 => convert_element_type_1
#   float_2 => convert_element_type_2
#   ge => ge
#   log => log
#   log_1 => log_1
#   loss => sub_2
#   mul_2 => mul_3
#   mul_3 => mul_4
#   mul_4 => mul_5
#   mul_5 => mul_6
#   mul_6 => mul_7
#   ne => ne
#   neg => neg
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   term1 => mul_1
#   term2 => mul_2
#   truediv => div
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%unsqueeze_1, %unsqueeze), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%convert_element_type_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 4), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%arg0_1, 1e-07), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_1,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %log), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %mul_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, 4), kwargs = {})
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Tensor](args = (%unsqueeze_1, %unsqueeze), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%unsqueeze_1, 0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%ne, %ge), kwargs = {})
#   %convert_element_type_2 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%mul_5, torch.float32), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 4), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_1, 1e-07), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add_2,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %log_1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convert_element_type_2, %mul_2), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, -3), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_4, %mul_7), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_2,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 4), kwargs = {})
triton_per_fused__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_sum_0 = async_compile.triton('triton_per_fused__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_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, 1024],
    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__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, '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__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 1024
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r3 = (rindex // 256)
    r5 = rindex % 64
    r0 = rindex % 4
    r7 = rindex % 256
    r4 = rindex
    tmp0 = tl.load(in_ptr0 + (r5 + (64*r3)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (r7), None, eviction_policy='evict_last')
    tmp1 = 1 + r0
    tmp2 = tmp1.to(tl.float32)
    tmp3 = tmp0 == tmp2
    tmp4 = tmp3.to(tl.float32)
    tmp5 = -tmp4
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = 1e-07
    tmp12 = tmp6 + tmp11
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp10 * tmp13
    tmp15 = tmp5 * tmp14
    tmp16 = 4.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp0 != tmp2
    tmp19 = 0.0
    tmp20 = tmp0 >= tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp6 * tmp6
    tmp24 = tmp23 * tmp23
    tmp25 = tmp8 + tmp11
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp24 * tmp26
    tmp28 = tmp22 * tmp27
    tmp29 = -3.0
    tmp30 = tmp28 * tmp29
    tmp31 = tmp17 - tmp30
    tmp32 = tl.broadcast_to(tmp31, [RBLOCK])
    tmp34 = triton_helpers.promote_to_tensor(tl.sum(tmp32, 0))
    tmp35 = 0.25
    tmp36 = tmp34 * tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp36, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [eq, float_1, neg, sub, pow_1, add, log, term1, mul_2, mul_3, ne, ge, mul_4, float_2, pow_2, sub_1, add_1, log_1, term2, mul_5, mul_6, loss, sum_1, truediv], Original ATen: [aten.eq, aten._to_copy, aten.neg, aten.rsub, aten.pow, aten.add, aten.log, aten.mul, aten.ne, aten.ge, aten.sub, aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_sum_0.run(buf2, arg1_1, arg0_1, 1, 1024, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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.utils.data
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__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_sum_0(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r3 = rindex // 256
    r5 = rindex % 64
    r0 = rindex % 4
    r7 = rindex % 256
    tmp0 = tl.load(in_ptr0 + (r5 + 64 * r3), None, eviction_policy='evict_last'
        )
    tmp6 = tl.load(in_ptr1 + r7, None, eviction_policy='evict_last')
    tmp1 = 1 + r0
    tmp2 = tmp1.to(tl.float32)
    tmp3 = tmp0 == tmp2
    tmp4 = tmp3.to(tl.float32)
    tmp5 = -tmp4
    tmp7 = 1.0
    tmp8 = tmp7 - tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = 1e-07
    tmp12 = tmp6 + tmp11
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp10 * tmp13
    tmp15 = tmp5 * tmp14
    tmp16 = 4.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp0 != tmp2
    tmp19 = 0.0
    tmp20 = tmp0 >= tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp6 * tmp6
    tmp24 = tmp23 * tmp23
    tmp25 = tmp8 + tmp11
    tmp26 = tl_math.log(tmp25)
    tmp27 = tmp24 * tmp26
    tmp28 = tmp22 * tmp27
    tmp29 = -3.0
    tmp30 = tmp28 * tmp29
    tmp31 = tmp17 - tmp30
    tmp32 = tl.broadcast_to(tmp31, [RBLOCK])
    tmp34 = triton_helpers.promote_to_tensor(tl.sum(tmp32, 0))
    tmp35 = 0.25
    tmp36 = tmp34 * tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp36, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused__to_copy_add_div_eq_ge_log_mul_ne_neg_pow_rsub_sub_sum_0[
            grid(1)](buf2, arg1_1, arg0_1, 1, 1024, num_warps=8, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class MySigmoidFocalLossNew(nn.Module):

    def __init__(self, gamma, alpha):
        super().__init__()
        self.gamma = gamma
        self.alpha = alpha

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