# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_1/inductor_cache/iz/cizel6izm3qkx5lyj557vl577qy64d5c3k6q5zrmzr4hnfoczj2w.py
# Topologically Sorted Source Nodes: [add, tanh], Original ATen: [aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   tanh => tanh
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %permute_3), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_tanh_0 = async_compile.triton('triton_poi_fused_add_tanh_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/hq/chqo6wzjtpel4egzrzr4xwgxayri6aa22rfqya3f4lqokw6sxt7a.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   input_6 => exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%squeeze, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/wp/cwpr2jp7bpqwkbaxgttcl2ekjdtkf6lezuspoiunutzd3j2qgeyk.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   input_6 => div_1, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/yv/cyvuloemaqj43mnhdenrcok4u3lpimupy4kn7s7u47a7nn67xd3i.py
# Topologically Sorted Source Nodes: [mul, output], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   output => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, %unsqueeze), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused_mul_sum_3 = async_compile.triton('triton_poi_fused_mul_sum_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        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  # reuse
        # Topologically Sorted Source Nodes: [add, tanh], Original ATen: [aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_tanh_0.run(buf2, primals_4, buf1, primals_6, 256, grid=grid(256), stream=stream0)
        del buf1
        del primals_4
        del primals_6
        buf3 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_3], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf3, buf4, 16, grid=grid(16), stream=stream0)
        buf5 = reinterpret_tensor(buf3, (4, 4), (4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf4, buf5, 16, grid=grid(16), stream=stream0)
        buf6 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [mul, output], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_3.run(primals_1, buf5, buf6, 16, grid=grid(16), stream=stream0)
    return (buf6, buf5, primals_1, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), buf2, buf5, primals_7, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from collections import OrderedDict
import torch.nn as nn


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 ContextAttentionLayer(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, reference: 'torch.Tensor', context: 'torch.Tensor'):
        """
        Forward pass through a context attention layer
        Arguments:
            reference (torch.Tensor): This is the reference input on which
                attention is computed.
                Shape: batch_size x ref_seq_length x ref_hidden_size
            context (torch.Tensor): This is the context used for attention.
                Shape: batch_size x context_seq_length x context_hidden_size
        Returns:
            (output, attention_weights):  A tuple of two Tensors, first one
                containing the reference filtered by attention (shape:
                batch_size x context_hidden_size x 1) and the second one the
                attention weights (batch_size x context_sequence_length x 1).
        """
        assert len(reference.shape) == 3, 'Reference tensor needs to be 3D'
        assert len(context.shape) == 3, 'Context tensor needs to be 3D'
        reference_attention = self.reference_projection(reference)
        context_attention = self.context_hidden_projection(self.
            context_projection(context).permute(0, 2, 1)).permute(0, 2, 1)
        alphas = self.alpha_projection(torch.tanh(reference_attention +
            context_attention))
        output = torch.sum(reference * torch.unsqueeze(alphas, -1), 1)
        return output, alphas


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


def get_init_inputs():
    return [[], {'reference_hidden_size': 4, 'reference_sequence_length': 4,
        'context_hidden_size': 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 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]
