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]
