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 numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.jit.quantized
import torch.onnx.operators
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, 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__softmax_1(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
    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 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    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)


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, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 4)](buf0, buf3, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(16, 4)](buf1, buf4, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf6
        buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_0[grid(16, 4)](buf2, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_0[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0)
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf10, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf11)
    return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
        ), buf7, 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
        ), buf7, reinterpret_tensor(buf10, (16, 4), (4, 1), 0
        ), primals_7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)


def combine_heads(X):
    """
    Combine heads (the inverse of split heads):
    1) Transpose X from (batch size, nheads, sequence length, d_head) to
        (batch size, sequence length, nheads, d_head)
    2) Combine (reshape) last 2 dimensions (nheads, d_head) into 1 (d_model)

    Inputs:
      X : [batch size * nheads, sequence length, d_head]
      nheads : integer
      d_head : integer

    Outputs:
      [batch_size, seq_len, d_model]

    """
    X = X.transpose(1, 2)
    nheads, d_head = X.shape[-2:]
    return X.contiguous().view(list(X.shape[:-2]) + [nheads * d_head])


def create_src_lengths_mask(batch_size, src_lengths):
    max_srclen = src_lengths.max()
    src_indices = torch.arange(0, max_srclen).unsqueeze(0).type_as(src_lengths)
    src_indices = src_indices.expand(batch_size, max_srclen)
    src_lengths = src_lengths.unsqueeze(dim=1).expand(batch_size, max_srclen)
    return (src_indices < src_lengths).int().detach()


def apply_masks(scores, batch_size, unseen_mask, src_lengths):
    seq_len = scores.shape[-1]
    sequence_mask = torch.ones(seq_len, seq_len).unsqueeze(0).int()
    if unseen_mask:
        sequence_mask = torch.tril(torch.ones(seq_len, seq_len), diagonal=0
            ).unsqueeze(0).int()
    if src_lengths is not None:
        src_lengths_mask = create_src_lengths_mask(batch_size=batch_size,
            src_lengths=src_lengths).unsqueeze(-2)
        sequence_mask = sequence_mask & src_lengths_mask
    sequence_mask = sequence_mask.unsqueeze(1)
    scores = scores.masked_fill(sequence_mask == 0, -np.inf)
    return scores


def scaled_dot_prod_attn(query, key, value, unseen_mask=False, src_lengths=None
    ):
    """
    Scaled Dot Product Attention

    Implements equation:
    Attention(Q, K, V) = softmax(QK^T/\\sqrt{d_k})V

    Inputs:
      query : [batch size, nheads, sequence length, d_k]
      key : [batch size, nheads, sequence length, d_k]
      value : [batch size, nheads, sequence length, d_v]
      unseen_mask: if True, only attend to previous sequence positions
      src_lengths_mask: if True, mask padding based on src_lengths

    Outputs:
      attn: [batch size, sequence length, d_v]

    Note that in this implementation d_q = d_k = d_v = dim
    """
    d_k = query.shape[-1]
    scores = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(d_k)
    if unseen_mask or src_lengths is not None:
        scores = apply_masks(scores=scores, batch_size=query.shape[0],
            unseen_mask=unseen_mask, src_lengths=src_lengths)
    p_attn = F.softmax(scores, dim=-1)
    return torch.matmul(p_attn, value), p_attn


def split_heads(X, nheads):
    """
    Split heads:
    1) Split (reshape) last dimension (size d_model) into nheads, d_head
    2) Transpose X from (batch size, sequence length, nheads, d_head) to
        (batch size, nheads, sequence length, d_head)

    Inputs:
      X : [batch size, sequence length, nheads * d_head]
      nheads : integer
    Outputs:
      [batch size,  nheads, sequence length, d_head]

    """
    last_dim = X.shape[-1]
    assert last_dim % nheads == 0
    X_last_dim_split = X.view(list(X.shape[:-1]) + [nheads, last_dim // nheads]
        )
    return X_last_dim_split.transpose(1, 2)


class MultiheadAttentionNew(nn.Module):
    """
    Multiheaded Scaled Dot Product Attention

    Implements equation:
    MultiHead(Q, K, V) = Concat(head_1,...,head_h)W^O
        where head_i = Attention(QW_i^Q, KW_i^K, VW_i^V)

    Similarly to the above, d_k = d_v = d_model / h

    Inputs
      init:
        nheads : integer # of attention heads
        d_model : model dimensionality
        d_head : dimensionality of a single head

      forward:
        query : [batch size, sequence length, d_model]
        key: [batch size, sequence length, d_model]
        value: [batch size, sequence length, d_model]
      unseen_mask: if True, only attend to previous sequence positions
      src_lengths_mask: if True, mask padding based on src_lengths

    Output
      result : [batch_size, sequence length, d_model]
    """

    def __init__(self, nheads, d_model):
        """Take in model size and number of heads."""
        super(MultiheadAttentionNew, self).__init__()
        assert d_model % nheads == 0
        self.d_head = d_model // nheads
        self.nheads = nheads
        self.Q_fc = nn.Linear(d_model, d_model, bias=False)
        self.K_fc = nn.Linear(d_model, d_model, bias=False)
        self.V_fc = nn.Linear(d_model, d_model, bias=False)
        self.output_fc = nn.Linear(d_model, d_model, bias=False)
        self.attn = None

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.Q_fc.weight
        primals_3 = self.K_fc.weight
        primals_5 = self.V_fc.weight
        primals_7 = self.output_fc.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]


Measured latency_ms: 0.04867