# 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/my/cmy3ptjdxy4kr3n5om5yjayav3eie3vcmckqm4vaoj4x2xupeiqs.py
# Topologically Sorted Source Nodes: [tp, sub, fp, sub_1, fn, sub_2, sub_3, tn], Original ATen: [aten.mul, aten.rsub]
# Source node to ATen node mapping:
#   fn => mul_2
#   fp => mul_1
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   tn => mul_3
#   tp => mul
# Graph fragment:
#   %mul : [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, %arg1_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %sub), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %arg1_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_2, %sub_3), kwargs = {})
triton_poi_fused_mul_rsub_0 = async_compile.triton('triton_poi_fused_mul_rsub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_rsub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp1
    tmp5 = tmp0 * tmp4
    tmp6 = tmp3 - tmp0
    tmp7 = tmp6 * tmp1
    tmp8 = tmp6 * tmp4
    tl.store(out_ptr0 + (x0), tmp2, xmask)
    tl.store(out_ptr1 + (x0), tmp5, xmask)
    tl.store(out_ptr2 + (x0), tmp7, xmask)
    tl.store(out_ptr3 + (x0), tmp8, xmask)
''', 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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [tp, sub, fp, sub_1, fn, sub_2, sub_3, tn], Original ATen: [aten.mul, aten.rsub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_rsub_0.run(arg0_1, arg1_1, buf0, buf1, buf2, buf3, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, buf1, buf2, 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 numpy as np
from torch import nn
import torch.jit
import torch.nn.functional


def sum_tensor(inp, axes, keepdim=False):
    axes = np.unique(axes).astype(int)
    if keepdim:
        for ax in axes:
            inp = inp.sum(int(ax), keepdim=True)
    else:
        for ax in sorted(axes, reverse=True):
            inp = inp.sum(int(ax))
    return inp


def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False):
    """
    net_output must be (b, c, x, y(, z)))
    gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
    if mask is provided it must have shape (b, 1, x, y(, z)))
    :param net_output:
    :param gt:
    :param axes: can be (, ) = no summation
    :param mask: mask must be 1 for valid pixels and 0 for invalid pixels
    :param square: if True then fp, tp and fn will be squared before summation
    :return:
    """
    if axes is None:
        axes = tuple(range(2, len(net_output.size())))
    shp_x = net_output.shape
    shp_y = gt.shape
    with torch.no_grad():
        if len(shp_x) != len(shp_y):
            gt = gt.view((shp_y[0], 1, *shp_y[1:]))
        if all([(i == j) for i, j in zip(net_output.shape, gt.shape)]):
            y_onehot = gt
        else:
            gt = gt.long()
            y_onehot = torch.zeros(shp_x)
            if net_output.device.type == 'cuda':
                y_onehot = y_onehot
            y_onehot.scatter_(1, gt, 1)
    tp = net_output * y_onehot
    fp = net_output * (1 - y_onehot)
    fn = (1 - net_output) * y_onehot
    tn = (1 - net_output) * (1 - y_onehot)
    if mask is not None:
        tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp,
            dim=1)), dim=1)
        fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp,
            dim=1)), dim=1)
        fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn,
            dim=1)), dim=1)
        tn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tn,
            dim=1)), dim=1)
    if square:
        tp = tp ** 2
        fp = fp ** 2
        fn = fn ** 2
        tn = tn ** 2
    if len(axes) > 0:
        tp = sum_tensor(tp, axes, keepdim=False)
        fp = sum_tensor(fp, axes, keepdim=False)
        fn = sum_tensor(fn, axes, keepdim=False)
        tn = sum_tensor(tn, axes, keepdim=False)
    return tp, fp, fn, tn


class GDL(nn.Module):

    def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
        smooth=1.0, square=False, square_volumes=False):
        """
        square_volumes will square the weight term. The paper recommends square_volumes=True; I don't (just an intuition)
        """
        super(GDL, self).__init__()
        self.square_volumes = square_volumes
        self.square = square
        self.do_bg = do_bg
        self.batch_dice = batch_dice
        self.apply_nonlin = apply_nonlin
        self.smooth = smooth

    def forward(self, x, y, loss_mask=None):
        shp_x = x.shape
        shp_y = y.shape
        if self.batch_dice:
            axes = [0] + list(range(2, len(shp_x)))
        else:
            axes = list(range(2, len(shp_x)))
        if len(shp_x) != len(shp_y):
            y = y.view((shp_y[0], 1, *shp_y[1:]))
        if all([(i == j) for i, j in zip(x.shape, y.shape)]):
            y_onehot = y
        else:
            gt = y.long()
            y_onehot = torch.zeros(shp_x)
            if x.device.type == 'cuda':
                y_onehot = y_onehot
            y_onehot.scatter_(1, gt, 1)
        if self.apply_nonlin is not None:
            x = self.apply_nonlin(x)
        if not self.do_bg:
            x = x[:, 1:]
            y_onehot = y_onehot[:, 1:]
        tp, fp, fn, _ = get_tp_fp_fn_tn(x, y_onehot, axes, loss_mask, self.
            square)
        volumes = sum_tensor(y_onehot, axes) + 1e-06
        if self.square_volumes:
            volumes = volumes ** 2
        tp = tp / volumes
        fp = fp / volumes
        fn = fn / volumes
        if self.batch_dice:
            axis = 0
        else:
            axis = 1
        tp = tp.sum(axis, keepdim=False)
        fp = fp.sum(axis, keepdim=False)
        fn = fn.sum(axis, keepdim=False)
        dc = (2 * tp + self.smooth) / (2 * tp + fp + fn + self.smooth)
        dc = dc.mean()
        return -dc


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
import numpy as np
from torch import nn
import torch.jit
import torch.nn.functional
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_mul_rsub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    out_ptr2, out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp1
    tmp5 = tmp0 * tmp4
    tmp6 = tmp3 - tmp0
    tmp7 = tmp6 * tmp1
    tmp8 = tmp6 * tmp4
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)
    tl.store(out_ptr2 + x0, tmp7, xmask)
    tl.store(out_ptr3 + x0, tmp8, xmask)


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((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_rsub_0[grid(256)](arg0_1, arg1_1, buf0, buf1,
            buf2, buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0, buf1, buf2, buf3


def sum_tensor(inp, axes, keepdim=False):
    axes = np.unique(axes).astype(int)
    if keepdim:
        for ax in axes:
            inp = inp.sum(int(ax), keepdim=True)
    else:
        for ax in sorted(axes, reverse=True):
            inp = inp.sum(int(ax))
    return inp


def get_tp_fp_fn_tn(net_output, gt, axes=None, mask=None, square=False):
    """
    net_output must be (b, c, x, y(, z)))
    gt must be a label map (shape (b, 1, x, y(, z)) OR shape (b, x, y(, z))) or one hot encoding (b, c, x, y(, z))
    if mask is provided it must have shape (b, 1, x, y(, z)))
    :param net_output:
    :param gt:
    :param axes: can be (, ) = no summation
    :param mask: mask must be 1 for valid pixels and 0 for invalid pixels
    :param square: if True then fp, tp and fn will be squared before summation
    :return:
    """
    if axes is None:
        axes = tuple(range(2, len(net_output.size())))
    shp_x = net_output.shape
    shp_y = gt.shape
    with torch.no_grad():
        if len(shp_x) != len(shp_y):
            gt = gt.view((shp_y[0], 1, *shp_y[1:]))
        if all([(i == j) for i, j in zip(net_output.shape, gt.shape)]):
            y_onehot = gt
        else:
            gt = gt.long()
            y_onehot = torch.zeros(shp_x)
            if net_output.device.type == 'cuda':
                y_onehot = y_onehot
            y_onehot.scatter_(1, gt, 1)
    tp = net_output * y_onehot
    fp = net_output * (1 - y_onehot)
    fn = (1 - net_output) * y_onehot
    tn = (1 - net_output) * (1 - y_onehot)
    if mask is not None:
        tp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tp,
            dim=1)), dim=1)
        fp = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fp,
            dim=1)), dim=1)
        fn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(fn,
            dim=1)), dim=1)
        tn = torch.stack(tuple(x_i * mask[:, 0] for x_i in torch.unbind(tn,
            dim=1)), dim=1)
    if square:
        tp = tp ** 2
        fp = fp ** 2
        fn = fn ** 2
        tn = tn ** 2
    if len(axes) > 0:
        tp = sum_tensor(tp, axes, keepdim=False)
        fp = sum_tensor(fp, axes, keepdim=False)
        fn = sum_tensor(fn, axes, keepdim=False)
        tn = sum_tensor(tn, axes, keepdim=False)
    return tp, fp, fn, tn


class GDLNew(nn.Module):

    def __init__(self, apply_nonlin=None, batch_dice=False, do_bg=True,
        smooth=1.0, square=False, square_volumes=False):
        """
        square_volumes will square the weight term. The paper recommends square_volumes=True; I don't (just an intuition)
        """
        super(GDLNew, self).__init__()
        self.square_volumes = square_volumes
        self.square = square
        self.do_bg = do_bg
        self.batch_dice = batch_dice
        self.apply_nonlin = apply_nonlin
        self.smooth = smooth

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