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 as F
import torch.nn as nn
import torch.nn.parallel
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_mean_0(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 16.0
    tmp2 = tmp0 / tmp1
    tmp4 = tmp3 / tmp1
    tmp6 = tmp5 / tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 / tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 / tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_per_fused__softmax_mse_loss_2(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + r2, None)
    tmp10 = tl.load(in_ptr1 + 4 * r1, None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tmp12 = tmp10 + tmp11
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp17 = tmp9 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
    tmp22 = tl.sum(tmp20, 1)[:, None]
    tmp23 = 16.0
    tmp24 = tmp22 / tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp24, 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((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](arg0_1, buf0, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = buf0
        del buf0
        triton_per_fused_mean_0[grid(16)](arg1_1, buf2, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf2
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused__softmax_mse_loss_2[grid(1)](buf6, buf1, buf3, 1, 
            16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
        del buf3
    return buf6,


def compute_kernel(x, y):
    x_size = x.size(0)
    y_size = y.size(0)
    dim = x.size(1)
    x = x.unsqueeze(1)
    y = y.unsqueeze(0)
    tiled_x = x.expand(x_size, y_size, dim)
    tiled_y = y.expand(x_size, y_size, dim)
    kernel_input = (tiled_x - tiled_y).pow(2).mean(2)
    return torch.exp(-kernel_input)


def mmd_loss(x, y, reduction=None):
    x_kernel = compute_kernel(x, x)
    y_kernel = compute_kernel(y, y)
    xy_kernel = compute_kernel(x, y)
    mmd = x_kernel.mean() + y_kernel.mean() - 2 * xy_kernel.mean()
    return mmd


def softmax_kl_loss(input_logits, target_logits, reduction='mean'):
    """Takes softmax on both sides and returns KL divergence

	Note:
	- Returns the sum over all examples. Divide by the batch size afterwards
	  if you want the mean.
	- Sends gradients to inputs but not the targets.
	"""
    input_log_softmax = F.log_softmax(input_logits, dim=1)
    target_softmax = F.softmax(target_logits, dim=1)
    return F.kl_div(input_log_softmax, target_softmax, reduction=reduction)


def softmax_mse_loss(input_logits, target_logits, reduction='mean'):
    """Takes softmax on both sides and returns MSE loss

	Note:
	- Returns the sum over all examples. Divide by the batch size afterwards
	  if you want the mean.
	- Sends gradients to inputs but not the targets.
	"""
    input_softmax = F.softmax(input_logits, dim=1)
    target_softmax = F.softmax(target_logits, dim=1)
    return F.mse_loss(input_softmax, target_softmax, reduction=reduction)


class Distribution_LossNew(nn.Module):

    def __init__(self, loss='softmax_mse', reduction='mean'):
        super(Distribution_LossNew, self).__init__()
        self.check_shape = True
        loss = loss.lower()
        if loss == 'mse':
            criterion = F.mse_loss
        elif loss == 'softmax_mse':
            criterion = softmax_mse_loss
        elif loss == 'kl':
            criterion = F.kl_div
        elif loss == 'softmax_kl':
            criterion = softmax_kl_loss
        elif loss == 'mmd':
            criterion = mmd_loss
            self.check_shape = False
        else:
            raise NotImplementedError
        self.loss_name = loss
        self.criterion = criterion
        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]


Measured latency_ms: 0.0274