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


class MultiheadAttention(nn.Module):
    """Multi-headed attention.
    See "Attention Is All You Need" for more details.
    """

    def __init__(self, embed_dim, num_heads, attn_dropout=0.0, bias=True,
        add_bias_kv=False, add_zero_attn=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.attn_dropout = attn_dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
        self.register_parameter('in_proj_bias', None)
        if bias:
            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        if add_bias_kv:
            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
        else:
            self.bias_k = self.bias_v = None
        self.add_zero_attn = add_zero_attn
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.in_proj_weight)
        nn.init.xavier_uniform_(self.out_proj.weight)
        if self.in_proj_bias is not None:
            nn.init.constant_(self.in_proj_bias, 0.0)
            nn.init.constant_(self.out_proj.bias, 0.0)
        if self.bias_k is not None:
            nn.init.xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            nn.init.xavier_normal_(self.bias_v)

    def forward(self, query, key, value, attn_mask=None):
        """Input shape: Time x Batch x Channel
        Self-attention can be implemented by passing in the same arguments for
        query, key and value. Timesteps can be masked by supplying a T x T mask in the
        `attn_mask` argument. Padding elements can be excluded from
        the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
        batch x src_len, where padding elements are indicated by 1s.
        """
        qkv_same = query.data_ptr() == key.data_ptr() == value.data_ptr()
        kv_same = key.data_ptr() == value.data_ptr()
        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == self.embed_dim
        assert list(query.size()) == [tgt_len, bsz, embed_dim]
        assert key.size() == value.size()
        if qkv_same:
            q, k, v = self.in_proj_qkv(query)
        elif kv_same:
            q = self.in_proj_q(query)
            if key is None:
                assert value is None
                k = v = None
            else:
                k, v = self.in_proj_kv(key)
        else:
            q = self.in_proj_q(query)
            k = self.in_proj_k(key)
            v = self.in_proj_v(value)
        q = self.scaling * q
        if self.bias_k is not None:
            assert self.bias_v is not None
            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(
                    attn_mask.size(0), 1)], dim=1)
        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim
            ).transpose(0, 1)
        if k is not None:
            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim
                ).transpose(0, 1)
        if v is not None:
            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim
                ).transpose(0, 1)
        src_len = k.size(1)
        if self.add_zero_attn:
            src_len += 1
            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])],
                dim=1)
            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])],
                dim=1)
            if attn_mask is not None:
                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(
                    attn_mask.size(0), 1)], dim=1)
        attn_weights = torch.bmm(q, k.transpose(1, 2))
        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len,
            src_len]
        if attn_mask is not None:
            try:
                attn_weights += attn_mask.unsqueeze(0)
            except:
                None
                None
                assert False
        attn_weights = F.softmax(attn_weights.float(), dim=-1).type_as(
            attn_weights)
        attn_weights = F.dropout(attn_weights, p=self.attn_dropout,
            training=self.training)
        attn = torch.bmm(attn_weights, v)
        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.
            head_dim]
        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        attn = self.out_proj(attn)
        attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
        attn_weights = attn_weights.sum(dim=1) / self.num_heads
        return attn, attn_weights

    def in_proj_qkv(self, query):
        return self._in_proj(query).chunk(3, dim=-1)

    def in_proj_kv(self, key):
        return self._in_proj(key, start=self.embed_dim).chunk(2, dim=-1)

    def in_proj_q(self, query, **kwargs):
        return self._in_proj(query, end=self.embed_dim, **kwargs)

    def in_proj_k(self, key):
        return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)

    def in_proj_v(self, value):
        return self._in_proj(value, start=2 * self.embed_dim)

    def _in_proj(self, input, start=0, end=None, **kwargs):
        weight = kwargs.get('weight', self.in_proj_weight)
        bias = kwargs.get('bias', self.in_proj_bias)
        weight = weight[start:end, :]
        if bias is not None:
            bias = bias[start:end]
        return F.linear(input, weight, bias)


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


def get_init_inputs():
    return [[], {'embed_dim': 4, 'num_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.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn import Parameter
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_mul_0(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
    tmp3 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    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 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tmp6 / tmp10
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp11, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_div_sum_3(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 % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 256 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0 + 256 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0 + 256 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (12,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (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_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 4),
            reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 16), alpha=1,
            beta=1, out=buf1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8),
            reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=1,
            beta=1, out=buf2)
        del primals_4
        buf3 = reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(64)](buf3, primals_5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((16, 4, 16), (64, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (1, 16, 0),
            0), reinterpret_tensor(buf1, (16, 1, 16), (1, 1, 16), 0), out=buf4)
        buf7 = empty_strided_cuda((16, 4, 16), (64, 16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(64)](buf4, buf7, 64, 16, XBLOCK=32,
            num_warps=4, num_stages=1)
        del buf4
        buf8 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf7, reinterpret_tensor(buf2, (16, 16, 1), (1, 
            16, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 16, 1), (16, 1, 1), torch.float32)
        triton_poi_fused_clone_2[grid(4, 16)](buf8, buf9, 4, 16, XBLOCK=16,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
        del buf8
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf9, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_7
        buf11 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_div_sum_3[grid(256)](buf7, buf11, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
    return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
        ), buf11, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf7, reinterpret_tensor(buf9, (16, 4), (4, 1), 0
        ), primals_6, reinterpret_tensor(buf2, (16, 1, 16), (1, 1, 16), 0
        ), reinterpret_tensor(buf3, (16, 1, 4), (1, 1, 16), 0
        ), reinterpret_tensor(buf1, (16, 16, 1), (1, 16, 1), 0)


class MultiheadAttentionNew(nn.Module):
    """Multi-headed attention.
    See "Attention Is All You Need" for more details.
    """

    def __init__(self, embed_dim, num_heads, attn_dropout=0.0, bias=True,
        add_bias_kv=False, add_zero_attn=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.attn_dropout = attn_dropout
        self.head_dim = embed_dim // num_heads
        assert self.head_dim * num_heads == self.embed_dim, 'embed_dim must be divisible by num_heads'
        self.scaling = self.head_dim ** -0.5
        self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
        self.register_parameter('in_proj_bias', None)
        if bias:
            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        if add_bias_kv:
            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
        else:
            self.bias_k = self.bias_v = None
        self.add_zero_attn = add_zero_attn
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.xavier_uniform_(self.in_proj_weight)
        nn.init.xavier_uniform_(self.out_proj.weight)
        if self.in_proj_bias is not None:
            nn.init.constant_(self.in_proj_bias, 0.0)
            nn.init.constant_(self.out_proj.bias, 0.0)
        if self.bias_k is not None:
            nn.init.xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            nn.init.xavier_normal_(self.bias_v)

    def in_proj_qkv(self, query):
        return self._in_proj(query).chunk(3, dim=-1)

    def in_proj_kv(self, key):
        return self._in_proj(key, start=self.embed_dim).chunk(2, dim=-1)

    def in_proj_q(self, query, **kwargs):
        return self._in_proj(query, end=self.embed_dim, **kwargs)

    def in_proj_k(self, key):
        return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)

    def in_proj_v(self, value):
        return self._in_proj(value, start=2 * self.embed_dim)

    def _in_proj(self, input, start=0, end=None, **kwargs):
        weight = kwargs.get('weight', self.in_proj_weight)
        bias = kwargs.get('bias', self.in_proj_bias)
        weight = weight[start:end, :]
        if bias is not None:
            bias = bias[start:end]
        return F.linear(input, weight, bias)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.in_proj_weight
        primals_5 = self.in_proj_bias
        primals_6 = self.out_proj.weight
        primals_7 = self.out_proj.bias
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
