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 libdevice, math as tl_math
from collections import OrderedDict
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_add_tanh_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    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 = 16
    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_mul_sum_3(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp14, 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), (16, 4, 1))
    assert_size_stride(primals_3, (16, 4), (4, 1))
    assert_size_stride(primals_4, (16,), (1,))
    assert_size_stride(primals_5, (16, 4), (4, 1))
    assert_size_stride(primals_6, (16,), (1,))
    assert_size_stride(primals_7, (1, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 16), (1, 4), 0), out=buf0)
        del primals_3
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = reinterpret_tensor(buf0, (4, 4, 16), (64, 16, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_tanh_0[grid(256)](buf2, primals_4, buf1,
            primals_6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf1
        del primals_4
        del primals_6
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_7, (16, 1), (1, 16), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(16)](buf3, buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0)
        del buf3
        triton_poi_fused__softmax_2[grid(16)](buf4, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused_mul_sum_3[grid(16)](primals_1, buf5, buf6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return buf6, buf5, primals_1, reinterpret_tensor(primals_2, (16, 4), (4,
        1), 0), buf2, buf5, primals_7


class Squeeze(nn.Module):
    """Squeeze wrapper for nn.Sequential."""

    def forward(self, data):
        return torch.squeeze(data)


class Temperature(nn.Module):
    """Temperature wrapper for nn.Sequential."""

    def __init__(self, temperature):
        super(Temperature, self).__init__()
        self.temperature = temperature

    def forward(self, data):
        return data / self.temperature


class ContextAttentionLayerNew(nn.Module):
    """
    Implements context attention as in the PaccMann paper (Figure 2C) in
    Molecular Pharmaceutics.
    With the additional option of having a hidden size in the context.
    NOTE:
    In tensorflow, weights were initialized from N(0,0.1). Instead, pytorch
    uses U(-stddev, stddev) where stddev=1./math.sqrt(weight.size(1)).
    """

    def __init__(self, reference_hidden_size: 'int',
        reference_sequence_length: 'int', context_hidden_size: 'int',
        context_sequence_length: 'int'=1, attention_size: 'int'=16,
        individual_nonlinearity: 'type'=nn.Sequential(), temperature:
        'float'=1.0):
        """Constructor
        Arguments:
            reference_hidden_size (int): Hidden size of the reference input
                over which the attention will be computed (H).
            reference_sequence_length (int): Sequence length of the reference
                (T).
            context_hidden_size (int): This is either simply the amount of
                features used as context (G) or, if the context is a sequence
                itself, the hidden size of each time point.
            context_sequence_length (int): Hidden size in the context, useful
                if context is also textual data, i.e. coming from nn.Embedding.
                Defaults to 1.
            attention_size (int): Hyperparameter of the attention layer,
                defaults to 16.
            individual_nonlinearities (type): This is an optional
                nonlinearity applied to each projection. Defaults to
                nn.Sequential(), i.e. no nonlinearity. Otherwise it expects a
                torch.nn activation function, e.g. nn.ReLU().
            temperature (float): Temperature parameter to smooth or sharpen the
                softmax. Defaults to 1. Temperature > 1 flattens the
                distribution, temperature below 1 makes it spikier.
        """
        super().__init__()
        self.reference_sequence_length = reference_sequence_length
        self.reference_hidden_size = reference_hidden_size
        self.context_sequence_length = context_sequence_length
        self.context_hidden_size = context_hidden_size
        self.attention_size = attention_size
        self.individual_nonlinearity = individual_nonlinearity
        self.temperature = temperature
        self.reference_projection = nn.Sequential(OrderedDict([(
            'projection', nn.Linear(reference_hidden_size, attention_size)),
            ('act_fn', individual_nonlinearity)]))
        self.context_projection = nn.Sequential(OrderedDict([('projection',
            nn.Linear(context_hidden_size, attention_size)), ('act_fn',
            individual_nonlinearity)]))
        if context_sequence_length > 1:
            self.context_hidden_projection = nn.Sequential(OrderedDict([(
                'projection', nn.Linear(context_sequence_length,
                reference_sequence_length)), ('act_fn',
                individual_nonlinearity)]))
        else:
            self.context_hidden_projection = nn.Sequential()
        self.alpha_projection = nn.Sequential(OrderedDict([('projection',
            nn.Linear(attention_size, 1, bias=False)), ('squeeze', Squeeze(
            )), ('temperature', Temperature(self.temperature)), ('softmax',
            nn.Softmax(dim=1))]))

    def forward(self, input_0, input_1):
        primals_3 = self.reference_projection.projection.weight
        primals_4 = self.reference_projection.projection.bias
        primals_5 = self.context_projection.projection.weight
        primals_6 = self.context_projection.projection.bias
        primals_7 = self.alpha_projection.projection.weight
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]


Measured latency_ms: 0.013882