# 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/io/ciova4kygjmtphlfmjc5zopchbwqwrxkqt77t37wnivl3mslnv6q.py
# Topologically Sorted Source Nodes: [transposed_1, target_sum, mul, clamp, truediv], Original ATen: [aten.clone, aten.sum, aten.mul, aten.clamp, aten.reciprocal]
# Source node to ATen node mapping:
#   clamp => clamp_min
#   mul => mul
#   target_sum => sum_1
#   transposed_1 => clone_1
#   truediv => mul_1, reciprocal
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%view_1, [-1]), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_1, %sum_1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 1e-05), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%clamp_min,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
triton_per_fused_clamp_clone_mul_reciprocal_sum_0 = async_compile.triton('triton_per_fused_clamp_clone_mul_reciprocal_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=[4, 64],
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_clamp_clone_mul_reciprocal_sum_0', '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_clamp_clone_mul_reciprocal_sum_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 16
    r2 = (rindex // 16)
    x0 = xindex
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0) + (64*r2)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = tmp4 * tmp4
    tmp6 = 1e-05
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.full([1, 1], 1, tl.int32)
    tmp9 = tmp8 / tmp7
    tmp10 = 1.0
    tmp11 = tmp9 * tmp10
    tl.store(out_ptr0 + (r3 + (64*x0)), tmp0, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/j4/cj43tvt2ej5tb4b45yykcp3vlqrhx3dhcrcrr3xxldx5ndw4whmn.py
# Topologically Sorted Source Nodes: [transposed], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   transposed => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[256], 
    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_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16) % 4
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2) + (64*x1)), xmask)
    tl.store(out_ptr0 + (x3), tmp0, 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, ), (1, ), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [transposed_1, target_sum, mul, clamp, truediv], Original ATen: [aten.clone, aten.sum, aten.mul, aten.clamp, aten.reciprocal]
        stream0 = get_raw_stream(0)
        triton_per_fused_clamp_clone_mul_reciprocal_sum_0.run(buf2, arg1_1, buf0, 4, 64, grid=grid(4), stream=stream0)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [transposed], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(arg0_1, buf3, 256, grid=grid(256), stream=stream0)
        del arg0_1
    return (buf2, reinterpret_tensor(buf3, (4, 64), (64, 1), 0), reinterpret_tensor(buf0, (4, 64), (64, 1), 0), )


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
import torch.utils.data
import torch
from torch.autograd import Variable


def flatten(tensor):
    """Flattens a given tensor such that the channel axis is first.
    The shapes are transformed as follows:
       (N, C, D, H, W) -> (C, N * D * H * W)
    """
    C = tensor.size(1)
    axis_order = (1, 0) + tuple(range(2, tensor.dim()))
    transposed = tensor.permute(axis_order).contiguous()
    return transposed.view(C, -1)


class GDiceLossV2(nn.Module):

    def __init__(self, apply_nonlin=None, smooth=1e-05):
        """
        Generalized Dice;
        Copy from: https://github.com/wolny/pytorch-3dunet/blob/6e5a24b6438f8c631289c10638a17dea14d42051/unet3d/losses.py#L75
        paper: https://arxiv.org/pdf/1707.03237.pdf
        tf code: https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py#L279
        """
        super(GDiceLossV2, self).__init__()
        self.apply_nonlin = apply_nonlin
        self.smooth = smooth

    def forward(self, net_output, gt):
        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)
        if self.apply_nonlin is not None:
            net_output = self.apply_nonlin(net_output)
        input = flatten(net_output)
        target = flatten(y_onehot)
        target = target.float()
        target_sum = target.sum(-1)
        class_weights = Variable(1.0 / (target_sum * target_sum).clamp(min=
            self.smooth), requires_grad=False)
        intersect = (input * target).sum(-1) * class_weights
        intersect = intersect.sum()
        denominator = ((input + target).sum(-1) * class_weights).sum()
        return -2.0 * intersect / denominator.clamp(min=self.smooth)


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
import torch.nn as nn
import torch.utils.data
import torch
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_per_fused_clamp_clone_mul_reciprocal_sum_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex % 16
    r2 = rindex // 16
    x0 = xindex
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0 + 64 * r2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = tmp4 * tmp4
    tmp6 = 1e-05
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tl.full([1, 1], 1, tl.int32)
    tmp9 = tmp8 / tmp7
    tmp10 = 1.0
    tmp11 = tmp9 * tmp10
    tl.store(out_ptr0 + (r3 + 64 * x0), tmp0, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp11, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, 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,), (1,), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_clamp_clone_mul_reciprocal_sum_0[grid(4)](buf2,
            arg1_1, buf0, 4, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](arg0_1, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf2, reinterpret_tensor(buf3, (4, 64), (64, 1), 0
        ), reinterpret_tensor(buf0, (4, 64), (64, 1), 0)


def flatten(tensor):
    """Flattens a given tensor such that the channel axis is first.
    The shapes are transformed as follows:
       (N, C, D, H, W) -> (C, N * D * H * W)
    """
    C = tensor.size(1)
    axis_order = (1, 0) + tuple(range(2, tensor.dim()))
    transposed = tensor.permute(axis_order).contiguous()
    return transposed.view(C, -1)


class GDiceLossV2New(nn.Module):

    def __init__(self, apply_nonlin=None, smooth=1e-05):
        """
        Generalized Dice;
        Copy from: https://github.com/wolny/pytorch-3dunet/blob/6e5a24b6438f8c631289c10638a17dea14d42051/unet3d/losses.py#L75
        paper: https://arxiv.org/pdf/1707.03237.pdf
        tf code: https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py#L279
        """
        super(GDiceLossV2New, self).__init__()
        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]
