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


class SelfAttentionRE(nn.Module):

    def __init__(self, emb_dim):
        super().__init__()
        self.query_mlp = nn.Linear(in_features=emb_dim, out_features=1)
        self.value_mlp = nn.Linear(in_features=emb_dim, out_features=emb_dim)

    def forward(self, x):
        B, N, N, emb_dim = x.shape
        value = self.value_mlp(x.view(-1, emb_dim)).view(B, N, N, emb_dim)
        query = self.query_mlp(x.view(-1, emb_dim)).view(B, N, N, 1)
        att_weights = F.softmax(query, dim=0)
        out = torch.sum(value * att_weights, dim=0)
        return out, att_weights.detach()


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


def get_init_inputs():
    return [[], {'emb_dim': 4}]

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
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import 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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), 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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_mul_sum_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    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)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp4 = tl.load(in_ptr1 + (16 + x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp8 = tl.load(in_ptr1 + (32 + x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp12 = tl.load(in_ptr1 + (48 + x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf2
        triton_poi_fused__softmax_1[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4), (16, 4, 1), 0)
        del buf3
        triton_poi_fused_mul_sum_2[grid(64)](buf0, buf4, buf5, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
    return buf5, buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), buf0, buf4


class SelfAttentionRENew(nn.Module):

    def __init__(self, emb_dim):
        super().__init__()
        self.query_mlp = nn.Linear(in_features=emb_dim, out_features=1)
        self.value_mlp = nn.Linear(in_features=emb_dim, out_features=emb_dim)

    def forward(self, input_0):
        primals_4 = self.query_mlp.weight
        primals_5 = self.query_mlp.bias
        primals_2 = self.value_mlp.weight
        primals_3 = self.value_mlp.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
