import math
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed


def matmul(x, y):
    if x.dim() == y.dim():
        return x @ y
    if x.dim() == y.dim() - 1:
        return (x.unsqueeze(-2) @ y).squeeze(-2)
    return (x @ y.unsqueeze(-2)).squeeze(-2)


class Attention(nn.Module):

    def __init__(self, d_key, drop_ratio, causal):
        super().__init__()
        self.scale = math.sqrt(d_key)
        self.dropout = nn.Dropout(drop_ratio)
        self.causal = causal

    def forward(self, query, key, value):
        dot_products = matmul(query, key.transpose(1, 2))
        if query.dim() == 3 and (self is None or self.causal):
            tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF
            if key.is_cuda:
                tri = tri
            dot_products.data.sub_(tri.unsqueeze(0))
        return matmul(self.dropout(F.softmax(dot_products / self.scale, dim
            =-1)), value)


class MultiHead(nn.Module):

    def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False):
        super().__init__()
        self.attention = Attention(d_key, drop_ratio, causal=causal)
        self.wq = nn.Linear(d_key, d_key, bias=False)
        self.wk = nn.Linear(d_key, d_key, bias=False)
        self.wv = nn.Linear(d_value, d_value, bias=False)
        self.wo = nn.Linear(d_value, d_key, bias=False)
        self.n_heads = n_heads

    def forward(self, query, key, value):
        query, key, value = self.wq(query), self.wk(key), self.wv(value)
        query, key, value = (x.chunk(self.n_heads, -1) for x in (query, key,
            value))
        return self.wo(torch.cat([self.attention(q, k, v) for q, k, v in
            zip(query, key, value)], -1))


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


def get_init_inputs():
    return [[], {'d_key': 4, 'd_value': 4, 'n_heads': 4, 'drop_ratio': 0.5}]

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 math
from torch import nn
import torch.nn.functional as F
import torch.utils.data.distributed
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
    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 = 1.0
    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 = 0.5
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x2, tmp17, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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_cat_2(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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + x1, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr2 + x1, tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr3 + x1, tmp16 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf5 = buf3
        del buf3
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf5, reinterpret_tensor(buf2, (4, 4, 1), (16, 4,
            1), 0), out=buf6)
        buf7 = buf4
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            1), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 1), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = buf7
        del buf7
        triton_poi_fused__softmax_1[grid(64)](buf8, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf9, reinterpret_tensor(buf2, (4, 4, 1), (16, 4,
            1), 1), out=buf10)
        buf11 = buf8
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            2), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 2), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf11, buf12, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = buf11
        del buf11
        triton_poi_fused__softmax_1[grid(64)](buf12, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf14 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf13, reinterpret_tensor(buf2, (4, 4, 1), (16, 
            4, 1), 2), out=buf14)
        buf15 = buf12
        del buf12
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 1), (16, 4, 1), 
            3), reinterpret_tensor(buf1, (4, 1, 4), (16, 1, 4), 3), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(64)](buf15, buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf17 = buf15
        del buf15
        triton_poi_fused__softmax_1[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf17, reinterpret_tensor(buf2, (4, 4, 1), (16, 
            4, 1), 3), out=buf18)
        buf19 = buf16
        del buf16
        triton_poi_fused_cat_2[grid(64)](buf6, buf10, buf14, buf18, buf19, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf10
        del buf14
        del buf18
        del buf6
        buf20 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf19, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf20)
    return reinterpret_tensor(buf20, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
        ), buf5, buf9, buf13, buf17, reinterpret_tensor(buf19, (16, 4), (4,
        1), 0), primals_7, reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 3
        ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 3
        ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 3
        ), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 2
        ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 2
        ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 2
        ), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 1
        ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 1
        ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 1
        ), reinterpret_tensor(buf2, (4, 1, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf0, (4, 1, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 1), (16, 4, 1), 0)


def matmul(x, y):
    if x.dim() == y.dim():
        return x @ y
    if x.dim() == y.dim() - 1:
        return (x.unsqueeze(-2) @ y).squeeze(-2)
    return (x @ y.unsqueeze(-2)).squeeze(-2)


class Attention(nn.Module):

    def __init__(self, d_key, drop_ratio, causal):
        super().__init__()
        self.scale = math.sqrt(d_key)
        self.dropout = nn.Dropout(drop_ratio)
        self.causal = causal

    def forward(self, query, key, value):
        dot_products = matmul(query, key.transpose(1, 2))
        if query.dim() == 3 and (self is None or self.causal):
            tri = torch.ones(key.size(1), key.size(1)).triu(1) * INF
            if key.is_cuda:
                tri = tri
            dot_products.data.sub_(tri.unsqueeze(0))
        return matmul(self.dropout(F.softmax(dot_products / self.scale, dim
            =-1)), value)


class MultiHeadNew(nn.Module):

    def __init__(self, d_key, d_value, n_heads, drop_ratio, causal=False):
        super().__init__()
        self.attention = Attention(d_key, drop_ratio, causal=causal)
        self.wq = nn.Linear(d_key, d_key, bias=False)
        self.wk = nn.Linear(d_key, d_key, bias=False)
        self.wv = nn.Linear(d_value, d_value, bias=False)
        self.wo = nn.Linear(d_value, d_key, bias=False)
        self.n_heads = n_heads

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.wq.weight
        primals_3 = self.wk.weight
        primals_5 = self.wv.weight
        primals_7 = self.wo.weight
        primals_2 = input_0
        primals_4 = input_1
        primals_6 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
