import math
import torch
import torch.nn as nn


class MultiHeadAttentionLayer(nn.Module):

    def __init__(self, d_model, n_heads, dropout):
        super().__init__()
        assert d_model % n_heads == 0
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.fc_q = nn.Linear(d_model, d_model)
        self.fc_k = nn.Linear(d_model, d_model)
        self.fc_v = nn.Linear(d_model, d_model)
        self.fc_o = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.scale = math.sqrt(d_model)

    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]
        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)
        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 
            2, 1, 3)
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 
            2, 1, 3)
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 
            2, 1, 3)
        K_t = K.permute(0, 1, 3, 2)
        energy = torch.matmul(Q, K_t) / self.scale
        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e+18)
        attention = torch.softmax(energy, dim=-1)
        x = torch.matmul(self.dropout(attention), V)
        x = x.permute(0, 2, 1, 3).contiguous()
        x = x.view(batch_size, -1, self.d_model)
        x = self.fc_o(x)
        return x, attention


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


def get_init_inputs():
    return [[], {'d_model': 4, 'n_heads': 4, 'dropout': 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
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_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    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 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    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 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float('-inf'))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp15, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    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 % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = 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, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_9, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf2)
        del primals_7
        buf3 = empty_strided_cuda((4, 4, 16, 1), (64, 16, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 16)](buf0, primals_3, buf3, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 16), (64, 16, 16, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(16, 16)](buf1, primals_5, buf4, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((16, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 16, 1), (16, 1, 0),
            0), reinterpret_tensor(buf4, (16, 1, 16), (16, 0, 1), 0), out=buf5)
        buf8 = empty_strided_cuda((4, 4, 16, 16), (1024, 256, 16, 1), torch
            .float32)
        triton_per_fused__softmax_1[grid(256)](buf5, buf8, 256, 16, XBLOCK=
            8, num_warps=2, num_stages=1)
        del buf5
        buf9 = reinterpret_tensor(buf1, (4, 4, 16, 1), (64, 16, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_0[grid(16, 16)](buf2, primals_8, buf9, 16, 
            16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_8
        buf10 = reinterpret_tensor(buf2, (16, 16, 1), (16, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf8, (16, 16, 16), (256, 16,
            1), 0), reinterpret_tensor(buf9, (16, 16, 1), (16, 1, 0), 0),
            out=buf10)
        buf11 = empty_strided_cuda((4, 16, 4, 1), (64, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(64, 4)](buf10, buf11, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf10, (64, 4), (4, 1), 0)
        del buf10
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf12)
        del primals_11
    return reinterpret_tensor(buf12, (4, 16, 4), (64, 4, 1), 0
        ), buf8, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_9, (64, 4), (4, 1), 0
        ), buf8, reinterpret_tensor(buf11, (64, 4), (4, 1), 0
        ), primals_10, reinterpret_tensor(buf9, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 16), (16, 1, 1), 0
        ), reinterpret_tensor(buf4, (16, 16, 1), (16, 1, 16), 0)


class MultiHeadAttentionLayerNew(nn.Module):

    def __init__(self, d_model, n_heads, dropout):
        super().__init__()
        assert d_model % n_heads == 0
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.fc_q = nn.Linear(d_model, d_model)
        self.fc_k = nn.Linear(d_model, d_model)
        self.fc_v = nn.Linear(d_model, d_model)
        self.fc_o = nn.Linear(d_model, d_model)
        self.dropout = nn.Dropout(dropout)
        self.scale = math.sqrt(d_model)

    def forward(self, input_0, input_1, input_2):
        primals_2 = self.fc_q.weight
        primals_3 = self.fc_q.bias
        primals_4 = self.fc_k.weight
        primals_5 = self.fc_k.bias
        primals_7 = self.fc_v.weight
        primals_8 = self.fc_v.bias
        primals_10 = self.fc_o.weight
        primals_11 = self.fc_o.bias
        primals_1 = input_0
        primals_6 = input_1
        primals_9 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0], output[1]
