import torch


class Attention(torch.nn.Module):
    """
    attention_size_1: Number of neurons in 1st attention layer.
    attention_size_2: Number of neurons in 2nd attention layer.        
    """

    def __init__(self, attention_size_1, attention_size_2):
        super(Attention, self).__init__()
        self.attention_1 = torch.nn.Linear(attention_size_1, attention_size_2)
        self.attention_2 = torch.nn.Linear(attention_size_2, attention_size_1)
    """
    Forward propagation pass
    
    gets x_in: Primary capsule output
    condensed_x: Attention normalized capsule output
    
    """

    def forward(self, x_in):
        attention_score_base = self.attention_1(x_in)
        attention_score_base = torch.nn.functional.relu(attention_score_base)
        attention_score = self.attention_2(attention_score_base)
        attention_score = torch.nn.functional.softmax(attention_score, dim=0)
        condensed_x = x_in * attention_score
        return condensed_x


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


def get_init_inputs():
    return [[], {'attention_size_1': 4, 'attention_size_2': 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
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_relu_threshold_backward_0(in_out_ptr0, 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
    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 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@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
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_2(in_ptr0, in_ptr1, 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
    x0 = xindex % 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = tmp1 / tmp8
    tmp10 = tmp0 * tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(256)](buf1,
            primals_2, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf2, buf3, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mul_2[grid(256)](primals_3, buf3, buf4, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf3
    return buf4, primals_3, reinterpret_tensor(buf1, (64, 4), (4, 1), 0
        ), buf2, primals_4, buf5


class AttentionNew(torch.nn.Module):
    """
    attention_size_1: Number of neurons in 1st attention layer.
    attention_size_2: Number of neurons in 2nd attention layer.        
    """

    def __init__(self, attention_size_1, attention_size_2):
        super(AttentionNew, self).__init__()
        self.attention_1 = torch.nn.Linear(attention_size_1, attention_size_2)
        self.attention_2 = torch.nn.Linear(attention_size_2, attention_size_1)
    """
    Forward propagation pass
    
    gets x_in: Primary capsule output
    condensed_x: Attention normalized capsule output
    
    """

    def forward(self, input_0):
        primals_1 = self.attention_1.weight
        primals_2 = self.attention_1.bias
        primals_4 = self.attention_2.weight
        primals_5 = self.attention_2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
