# 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_7/inductor_cache/nf/cnfbmcj4qjpw42wcretgofguvl5ncchceie2terfp3l6x5zgs2fl.py
# Topologically Sorted Source Nodes: [eq, y_pred_1, ones_like, pt_1, sub, pow_1, mul_4, log, mul_5, mean, neg, eq_1, zeros_like, pt_0, pow_2, mul_6, sub_1, log_1, mul_7, mean_1, focal_loss, mul, intersection, mul_1, add, mul_2, sum_2, mul_3, sum_3, add_1, add_2, dice_loss, log_2, sub_3], Original ATen: [aten.eq, aten.clamp, aten.ones_like, aten.where, aten.rsub, aten.pow, aten.mul, aten.log, aten.mean, aten.neg, aten.zeros_like, aten.sub, aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   dice_loss => div
#   eq => eq
#   eq_1 => eq_1
#   focal_loss => sub_2
#   intersection => sum_1
#   log => log
#   log_1 => log_1
#   log_2 => log_2
#   mean => mean
#   mean_1 => mean_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   neg => neg
#   ones_like => full_default
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pt_0 => where_1
#   pt_1 => where
#   sub => sub
#   sub_1 => sub_1
#   sub_3 => sub_3
#   sum_2 => sum_2
#   sum_3 => sum_3
#   y_pred_1 => clamp_min
#   zeros_like => full_default_1
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%view, 1), kwargs = {})
#   %clamp_min : [num_users=2] = call_function[target=torch.ops.aten.clamp_min.default](args = (%view_1, 1e-07), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([256], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq, %clamp_min, %full_default), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %where), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 1.1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, 0.48), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%where,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %log), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_5,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean,), kwargs = {})
#   %eq_1 : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%view, 0), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([256], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%eq_1, %clamp_min, %full_default_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%where_1, 1.1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, 0.52), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %where_1), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_1,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %log_1), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_7,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%neg, %mean_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, 2.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, 1.0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_2,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %view_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul_3,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %sum_3), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, 1.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_2), kwargs = {})
#   %log_2 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div,), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %log_2), kwargs = {})
triton_per_fused_add_clamp_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_0 = async_compile.triton('triton_per_fused_add_clamp_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_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, 256],
    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_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 5, '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_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp3 = tl.load(in_ptr1 + (r0), None)
    tmp1 = 1.0
    tmp2 = tmp0 == tmp1
    tmp4 = 1e-07
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tl.where(tmp2, tmp5, tmp1)
    tmp7 = tmp1 - tmp6
    tmp8 = 1.1
    tmp9 = libdevice.pow(tmp7, tmp8)
    tmp10 = 0.48
    tmp11 = tmp9 * tmp10
    tmp12 = tl_math.log(tmp6)
    tmp13 = tmp11 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 0.0
    tmp18 = tmp0 == tmp17
    tmp19 = tl.where(tmp18, tmp5, tmp17)
    tmp20 = libdevice.pow(tmp19, tmp8)
    tmp21 = 0.52
    tmp22 = tmp20 * tmp21
    tmp23 = tmp1 - tmp19
    tmp24 = tl_math.log(tmp23)
    tmp25 = tmp22 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = tmp0 * tmp3
    tmp30 = tl.broadcast_to(tmp29, [RBLOCK])
    tmp32 = triton_helpers.promote_to_tensor(tl.sum(tmp30, 0))
    tmp33 = tmp0 * tmp0
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp37 = tmp3 * tmp3
    tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
    tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
    tmp41 = 256.0
    tmp42 = tmp16 / tmp41
    tmp43 = -tmp42
    tmp44 = tmp28 / tmp41
    tmp45 = tmp43 - tmp44
    tmp46 = 2.0
    tmp47 = tmp32 * tmp46
    tmp48 = tmp47 + tmp1
    tmp49 = tmp36 + tmp40
    tmp50 = tmp49 + tmp1
    tmp51 = tmp48 / tmp50
    tmp52 = tl_math.log(tmp51)
    tmp53 = tmp45 - tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp53, 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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [eq, y_pred_1, ones_like, pt_1, sub, pow_1, mul_4, log, mul_5, mean, neg, eq_1, zeros_like, pt_0, pow_2, mul_6, sub_1, log_1, mul_7, mean_1, focal_loss, mul, intersection, mul_1, add, mul_2, sum_2, mul_3, sum_3, add_1, add_2, dice_loss, log_2, sub_3], Original ATen: [aten.eq, aten.clamp, aten.ones_like, aten.where, aten.rsub, aten.pow, aten.mul, aten.log, aten.mean, aten.neg, aten.zeros_like, aten.sub, aten.sum, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_0.run(buf5, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf5, )


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 torch.nn as nn
from torch import optim as optim


class EMLLoss(nn.Module):

    def __init__(self):
        super(EMLLoss, self).__init__()

    def forward(self, y_pred, y_true):
        gamma = 1.1
        alpha = 0.48
        smooth = 1.0
        epsilon = 1e-07
        y_true = y_true.view(-1)
        y_pred = y_pred.view(-1)
        intersection = (y_true * y_pred).sum()
        dice_loss = (2.0 * intersection + smooth) / ((y_true * y_true).sum(
            ) + (y_pred * y_pred).sum() + smooth)
        y_pred = torch.clamp(y_pred, epsilon)
        pt_1 = torch.where(torch.eq(y_true, 1), y_pred, torch.ones_like(y_pred)
            )
        pt_0 = torch.where(torch.eq(y_true, 0), y_pred, torch.zeros_like(
            y_pred))
        focal_loss = -torch.mean(alpha * torch.pow(1.0 - pt_1, gamma) *
            torch.log(pt_1)) - torch.mean((1 - alpha) * torch.pow(pt_0,
            gamma) * torch.log(1.0 - pt_0))
        return focal_loss - torch.log(dice_loss)


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


def get_init_inputs():
    return [[], {}]

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 libdevice, math as tl_math
import torch.nn as nn
from torch import optim as optim
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_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_0(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    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)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 == tmp1
    tmp4 = 1e-07
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = tl.where(tmp2, tmp5, tmp1)
    tmp7 = tmp1 - tmp6
    tmp8 = 1.1
    tmp9 = libdevice.pow(tmp7, tmp8)
    tmp10 = 0.48
    tmp11 = tmp9 * tmp10
    tmp12 = tl_math.log(tmp6)
    tmp13 = tmp11 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 0.0
    tmp18 = tmp0 == tmp17
    tmp19 = tl.where(tmp18, tmp5, tmp17)
    tmp20 = libdevice.pow(tmp19, tmp8)
    tmp21 = 0.52
    tmp22 = tmp20 * tmp21
    tmp23 = tmp1 - tmp19
    tmp24 = tl_math.log(tmp23)
    tmp25 = tmp22 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = triton_helpers.promote_to_tensor(tl.sum(tmp26, 0))
    tmp29 = tmp0 * tmp3
    tmp30 = tl.broadcast_to(tmp29, [RBLOCK])
    tmp32 = triton_helpers.promote_to_tensor(tl.sum(tmp30, 0))
    tmp33 = tmp0 * tmp0
    tmp34 = tl.broadcast_to(tmp33, [RBLOCK])
    tmp36 = triton_helpers.promote_to_tensor(tl.sum(tmp34, 0))
    tmp37 = tmp3 * tmp3
    tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
    tmp40 = triton_helpers.promote_to_tensor(tl.sum(tmp38, 0))
    tmp41 = 256.0
    tmp42 = tmp16 / tmp41
    tmp43 = -tmp42
    tmp44 = tmp28 / tmp41
    tmp45 = tmp43 - tmp44
    tmp46 = 2.0
    tmp47 = tmp32 * tmp46
    tmp48 = tmp47 + tmp1
    tmp49 = tmp36 + tmp40
    tmp50 = tmp49 + tmp1
    tmp51 = tmp48 / tmp50
    tmp52 = tl_math.log(tmp51)
    tmp53 = tmp45 - tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp53, 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)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_clamp_div_eq_log_mean_mul_neg_ones_like_pow_rsub_sub_sum_where_zeros_like_0[
            grid(1)](buf5, arg0_1, arg1_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf5,


class EMLLossNew(nn.Module):

    def __init__(self):
        super(EMLLossNew, self).__init__()

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