import torch
import torch.nn.functional
import torch.nn as nn
import torch.nn.functional as F


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class WeightedMCEloss(nn.Module):

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

    def forward(self, y_pred, y_true, weight):
        _n, _ch, h, w = y_pred.size()
        y_true = centercrop(y_true, w, h)
        weight = centercrop(weight, w, h)
        y_pred_log = F.log_softmax(y_pred, dim=1)
        logpy = torch.sum(weight * y_pred_log * y_true, dim=1)
        loss = -torch.mean(logpy)
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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 math as tl_math
import torch.nn.functional
import torch.nn as nn
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__log_softmax_0(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_sum_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, 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)
    tmp3 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp6 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp17 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp20 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp23 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp29 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tmp0 * tmp13
    tmp16 = tmp14 * tmp15
    tmp18 = tmp3 - tmp12
    tmp19 = tmp17 * tmp18
    tmp21 = tmp19 * tmp20
    tmp22 = tmp16 + tmp21
    tmp24 = tmp6 - tmp12
    tmp25 = tmp23 * tmp24
    tmp27 = tmp25 * tmp26
    tmp28 = tmp22 + tmp27
    tmp30 = tmp9 - tmp12
    tmp31 = tmp29 * tmp30
    tmp33 = tmp31 * tmp32
    tmp34 = tmp28 + tmp33
    tmp35 = tl.broadcast_to(tmp34, [XBLOCK, RBLOCK])
    tmp37 = tl.sum(tmp35, 1)[:, None]
    tmp38 = 64.0
    tmp39 = tmp37 / tmp38
    tmp40 = -tmp39
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp40, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf3,
            arg2_1, buf0, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del arg2_1
        del buf0
    return buf3,


def centercrop(image, w, h):
    _nt, _ct, ht, wt = image.size()
    padw, padh = (wt - w) // 2, (ht - h) // 2
    if padw > 0 and padh > 0:
        image = image[:, :, padh:-padh, padw:-padw]
    return image


class WeightedMCElossNew(nn.Module):

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

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
