import torch
import torch.nn as nn


class SelfAttention(nn.Module):

    def __init__(self, embed_size, heads):
        super(SelfAttention, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        assert self.head_dim * heads == embed_size, 'Embedding size needs to be divisible by heads'
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(heads * self.head_dim, embed_size)

    def forward(self, values, keys, query, mask):
        N = query.shape[0]
        value_len, key_len, query_len = values.shape[1], keys.shape[1
            ], query.shape[1]
        values = values.reshape(N, value_len, self.heads, self.head_dim)
        keys = keys.reshape(N, key_len, self.heads, self.head_dim)
        query = query.reshape(N, query_len, self.heads, self.head_dim)
        values = self.values(values)
        keys = self.keys(keys)
        queries = self.queries(query)
        energy = torch.einsum('nqhd,nkhd->nhqk', [queries, keys])
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float('-1e20'))
        attention = torch.softmax(energy / self.embed_size ** (1 / 2), dim=3)
        out = torch.einsum('nhql,nlhd->nqhd', [attention, values]).reshape(N,
            query_len, self.heads * self.head_dim)
        out = self.fc_out(out)
        return out


def get_inputs():
    return [torch.rand([4, 4, 4, 1]), torch.rand([4, 4, 4, 1]), torch.rand(
        [4, 4, 4, 1]), torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'embed_size': 4, 'heads': 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
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_eq_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp9 = tl.load(in_ptr2 + (4 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (2 + 4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp15 = tl.load(in_ptr2 + (8 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x2 + 16 * y3), xmask & ymask,
        eviction_policy='evict_last').to(tl.int1)
    tmp21 = tl.load(in_ptr2 + (12 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = -1.0000000200408773e+20
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp10 = tmp1 * tmp9
    tmp11 = tl.where(tmp8, tmp4, tmp10)
    tmp12 = tmp11 * tmp6
    tmp13 = triton_helpers.maximum(tmp7, tmp12)
    tmp16 = tmp1 * tmp15
    tmp17 = tl.where(tmp14, tmp4, tmp16)
    tmp18 = tmp17 * tmp6
    tmp19 = triton_helpers.maximum(tmp13, tmp18)
    tmp22 = tmp1 * tmp21
    tmp23 = tl.where(tmp20, tmp4, tmp22)
    tmp24 = tmp23 * tmp6
    tmp25 = triton_helpers.maximum(tmp19, tmp24)
    tmp26 = tmp7 - tmp25
    tmp27 = 0.5
    tmp28 = tmp26 * tmp27
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp12 - tmp25
    tmp31 = tmp30 * tmp27
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp29 + tmp32
    tmp34 = tmp18 - tmp25
    tmp35 = tmp34 * tmp27
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp33 + tmp36
    tmp38 = tmp24 - tmp25
    tmp39 = tmp38 * tmp27
    tmp40 = tl_math.exp(tmp39)
    tmp41 = tmp37 + tmp40
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp25, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 4 * y3), tmp41, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x0 = xindex % 4
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x4, xmask).to(tl.int1)
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * x1 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x2 + 4 * x0 + 16 * x3), xmask,
        eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr3 + x5, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr4 + x5, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 * tmp2
    tmp4 = -1.0000000200408773e+20
    tmp5 = tl.where(tmp0, tmp4, tmp3)
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = 0.5
    tmp11 = tmp9 * tmp10
    tmp12 = tl_math.exp(tmp11)
    tmp14 = tmp12 / tmp13
    tl.store(out_ptr0 + x4, tmp14, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_4(in_out_ptr0, in_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 % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_2, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_3, (4, 4, 4, 1), (16, 4, 1, 1))
    assert_size_stride(primals_4, (1, 1), (1, 1))
    assert_size_stride(primals_5, (1, 1), (1, 1))
    assert_size_stride(primals_6, (1, 1), (1, 1))
    assert_size_stride(primals_7, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 1), (1, 1), 0),
            primals_4, out=buf0)
        del primals_4
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 1), (1, 1), 0),
            primals_5, out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 1), (1, 1), 0),
            primals_6, out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_eq_0[grid(256)](primals_7, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_7
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_masked_fill_1[grid(16, 4)](buf3, buf2,
            buf1, buf4, buf5, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1,
            num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_masked_fill_2[grid(256)](buf3, buf2, buf1,
            buf4, buf5, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 1, 1), (16, 4, 1, 1, 1), 0)
        del buf5
        triton_poi_fused_clone_3[grid(16, 4)](buf0, buf7, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf8 = reinterpret_tensor(buf0, (16, 4, 1), (4, 1, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 0), 0), out=buf8)
        buf9 = reinterpret_tensor(buf4, (4, 4, 4), (16, 4, 1), 0)
        del buf4
        triton_poi_fused_clone_3[grid(16, 4)](buf8, buf9, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
        del buf8
        extern_kernels.mm(reinterpret_tensor(buf9, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0)
        del buf10
        triton_poi_fused_add_4[grid(64)](buf11, primals_9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_9
    return buf11, reinterpret_tensor(primals_2, (64, 1), (1, 1), 0
        ), reinterpret_tensor(primals_3, (64, 1), (1, 1), 0
        ), buf1, reinterpret_tensor(primals_1, (64, 1), (1, 1), 0
        ), buf2, buf3, buf6, reinterpret_tensor(buf9, (16, 4), (4, 1), 0
        ), primals_8, reinterpret_tensor(buf7, (16, 1, 4), (4, 1, 1), 0)


class SelfAttentionNew(nn.Module):

    def __init__(self, embed_size, heads):
        super(SelfAttentionNew, self).__init__()
        self.embed_size = embed_size
        self.heads = heads
        self.head_dim = embed_size // heads
        assert self.head_dim * heads == embed_size, 'Embedding size needs to be divisible by heads'
        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
        self.fc_out = nn.Linear(heads * self.head_dim, embed_size)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_4 = self.values.weight
        primals_5 = self.keys.weight
        primals_6 = self.queries.weight
        primals_8 = self.fc_out.weight
        primals_9 = self.fc_out.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_7 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
