# 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/or/cor6hcedlmhgvyzt23wo6pgsgs7o4d2usa6e73qf5w7rrg6tgb3f.py
# Topologically Sorted Source Nodes: [mse_loss, add, mse_loss_1, add_1, mse_loss_2, add_2, mse_loss_3, add_3], Original ATen: [aten.mse_loss, aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_3 => add_3
#   mse_loss => mean, pow_1, sub
#   mse_loss_1 => mean_1, pow_2, sub_1
#   mse_loss_2 => mean_2, pow_3, sub_2
#   mse_loss_3 => mean_3, pow_4, sub_3
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem, %getitem_4), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, 0), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem_1, %getitem_5), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_2,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %mean_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem_2, %getitem_6), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mean_2), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%getitem_3, %getitem_7), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_3, 2), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_4,), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mean_3), kwargs = {})
triton_per_fused_add_mse_loss_0 = async_compile.triton('triton_per_fused_add_mse_loss_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, 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': {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_mse_loss_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 4, '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_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr1 + (r0 + (64*r1)), None)
    tmp7 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp8 = tl.load(in_ptr1 + (16 + r0 + (64*r1)), None)
    tmp14 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp15 = tl.load(in_ptr1 + (32 + r0 + (64*r1)), None)
    tmp21 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp22 = tl.load(in_ptr1 + (48 + r0 + (64*r1)), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.sum(tmp18, 1)[:, None]
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp28 = 64.0
    tmp29 = tmp6 / tmp28
    tmp30 = 0.0
    tmp31 = tmp29 + tmp30
    tmp32 = tmp13 / tmp28
    tmp33 = tmp31 + tmp32
    tmp34 = tmp20 / tmp28
    tmp35 = tmp33 + tmp34
    tmp36 = tmp27 / tmp28
    tmp37 = tmp35 + tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp37, 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)
        buf4 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [mse_loss, add, mse_loss_1, add_1, mse_loss_2, add_2, mse_loss_3, add_3], Original ATen: [aten.mse_loss, aten.add]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_mse_loss_0.run(buf4, arg0_1, arg1_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (reinterpret_tensor(buf4, (1, ), (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)

from torch.nn import Module
import torch
from torch import Tensor
from torch.nn import MSELoss


def _split_masks_by_classes(pred: 'Tensor', target: 'Tensor') ->[]:
    """
    Split masks by classes

    Args:
        pred (Tensor): predicted masks of shape [B, C, H, W]
        target (Tensor): target masks of shape [B, C, H, W]

    Returns:
        List: list of masks pairs [pred, target], splitted by channels. List shape: [C, 2, B, H, W]
    """
    preds = torch.split(pred, 1, dim=1)
    targets = torch.split(target, 1, dim=1)
    return list(zip(preds, targets))


class Reduction:

    def __init__(self, method: 'str'='sum'):
        super().__init__()
        if method == 'sum':
            self._reduction = lambda x: x.sum(0)
            self._list_reduction = lambda x: sum(x)
        elif method == 'mean':
            self._reduction = lambda x: x.sum(0)
            self._list_reduction = lambda x: sum(x) / len(x)
        else:
            raise Exception(
                "Unexpected reduction '{}'. Possible values: [sum, mean]".
                format(method))

    def __call__(self, data):
        return self._reduction(data).unsqueeze(0)

    def reduct_list(self, data):
        return self._list_reduction(data).unsqueeze(0)


class MulticlassSegmentationLoss(Module):
    """
    Wrapper loss function to work with multiclass inference.
    This just split masks by classes and calculate :arg:`base_loss` for every class. After that all loss values summarized

    Args:
         base_loss (Module): basic loss object
    """

    def __init__(self, base_loss: 'Module', reduction: 'Reduction'=
        Reduction('sum')):
        super().__init__()
        self._base_loss = base_loss
        self._reduction = reduction

    def forward(self, output: 'Tensor', target: 'Tensor'):
        res = []
        for i, [p, t] in enumerate(_split_masks_by_classes(output, target)):
            res.append(self._base_loss(p, t))
        return self._reduction.reduct_list(res)


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


def get_init_inputs():
    return [[], {'base_loss': MSELoss()}]

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.nn import Module
from torch import Tensor
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_add_mse_loss_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp7 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp21 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp22 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.sum(tmp11, 1)[:, None]
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK, RBLOCK])
    tmp20 = tl.sum(tmp18, 1)[:, None]
    tmp23 = tmp21 - tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.sum(tmp25, 1)[:, None]
    tmp28 = 64.0
    tmp29 = tmp6 / tmp28
    tmp30 = 0.0
    tmp31 = tmp29 + tmp30
    tmp32 = tmp13 / tmp28
    tmp33 = tmp31 + tmp32
    tmp34 = tmp20 / tmp28
    tmp35 = tmp33 + tmp34
    tmp36 = tmp27 / tmp28
    tmp37 = tmp35 + tmp36
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp37, 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)
        buf4 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_mse_loss_0[grid(1)](buf4, arg0_1, arg1_1, 1, 
            64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return reinterpret_tensor(buf4, (1,), (1,), 0),


def _split_masks_by_classes(pred: 'Tensor', target: 'Tensor') ->[]:
    """
    Split masks by classes

    Args:
        pred (Tensor): predicted masks of shape [B, C, H, W]
        target (Tensor): target masks of shape [B, C, H, W]

    Returns:
        List: list of masks pairs [pred, target], splitted by channels. List shape: [C, 2, B, H, W]
    """
    preds = torch.split(pred, 1, dim=1)
    targets = torch.split(target, 1, dim=1)
    return list(zip(preds, targets))


class Reduction:

    def __init__(self, method: 'str'='sum'):
        super().__init__()
        if method == 'sum':
            self._reduction = lambda x: x.sum(0)
            self._list_reduction = lambda x: sum(x)
        elif method == 'mean':
            self._reduction = lambda x: x.sum(0)
            self._list_reduction = lambda x: sum(x) / len(x)
        else:
            raise Exception(
                "Unexpected reduction '{}'. Possible values: [sum, mean]".
                format(method))

    def __call__(self, data):
        return self._reduction(data).unsqueeze(0)

    def reduct_list(self, data):
        return self._list_reduction(data).unsqueeze(0)


class MulticlassSegmentationLossNew(Module):
    """
    Wrapper loss function to work with multiclass inference.
    This just split masks by classes and calculate :arg:`base_loss` for every class. After that all loss values summarized

    Args:
         base_loss (Module): basic loss object
    """

    def __init__(self, base_loss: 'Module', reduction: 'Reduction'=
        Reduction('sum')):
        super().__init__()
        self._base_loss = base_loss
        self._reduction = reduction

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