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


def gram_matrix(input):
    """ gram matrix for feature assignments """
    a, b, c, d = input.size()
    allG = []
    for i in range(a):
        features = input[i].view(b, c * d)
        gram = torch.mm(features, features.t())
        gram = gram.div(c * d)
        allG.append(gram)
    return torch.stack(allG)


class GetStyleLoss(nn.Module):
    """ evaluate the style loss with gram matrix """

    def forward(self, input, target):
        """ forward pass """
        gram = gram_matrix(target)
        return F.mse_loss(gram, input)


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


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
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_poi_fused_stack_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1), tmp4 & xmask, other=0.0)
    tmp6 = 0.0625
    tmp7 = tmp5 * tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1)), tmp13 & xmask, other=0.0)
    tmp15 = tmp14 * tmp6
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp13, tmp15, tmp16)
    tmp18 = tmp0 >= tmp11
    tmp19 = tl.full([1], 12, tl.int64)
    tmp20 = tmp0 < tmp19
    tmp21 = tmp18 & tmp20
    tmp22 = tl.load(in_ptr2 + (x0 + 4 * (-8 + x1)), tmp21 & xmask, other=0.0)
    tmp23 = tmp22 * tmp6
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp21, tmp23, tmp24)
    tmp26 = tmp0 >= tmp19
    tl.full([1], 16, tl.int64)
    tmp29 = tl.load(in_ptr3 + (x0 + 4 * (-12 + x1)), tmp26 & xmask, other=0.0)
    tmp30 = tmp29 * tmp6
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp26, tmp30, tmp31)
    tmp33 = tl.where(tmp21, tmp25, tmp32)
    tmp34 = tl.where(tmp13, tmp17, tmp33)
    tmp35 = tl.where(tmp4, tmp9, tmp34)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_per_fused_mse_loss_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 64
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + r2, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp7 = 256.0
    tmp8 = tmp6 / tmp7
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp8, 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), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 0),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 64),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 64), out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 128),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 128), out=buf2)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(arg0_1, (4, 16), (16, 1), 192),
            reinterpret_tensor(arg0_1, (16, 4), (1, 16), 192), out=buf3)
        del arg0_1
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(64)](buf0, buf1, buf2, buf3, buf4, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_mse_loss_1[grid(1)](buf6, buf4, arg1_1, 1, 256,
            num_warps=2, num_stages=1)
        del arg1_1
        del buf4
    return buf6,


def gram_matrix(input):
    """ gram matrix for feature assignments """
    a, b, c, d = input.size()
    allG = []
    for i in range(a):
        features = input[i].view(b, c * d)
        gram = torch.mm(features, features.t())
        gram = gram.div(c * d)
        allG.append(gram)
    return torch.stack(allG)


class GetStyleLossNew(nn.Module):
    """ evaluate the style loss with gram matrix """

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