import torch
import torch.nn as nn


class FactorizedSynthesizerDense(nn.Module):

    def __init__(self, in_dims, sentence_length):
        super(FactorizedSynthesizerDense, self).__init__()
        self.a = 4
        self.b = sentence_length // self.a
        self.a_proj = nn.Linear(in_dims, self.a)
        self.b_proj = nn.Linear(in_dims, self.b)
        self.value_fc = nn.Linear(in_dims, in_dims)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, x):
        A = self.a_proj(x).repeat([1, 1, self.b])
        B = self.b_proj(x).repeat([1, 1, self.a])
        energy = A * B
        attention = self.softmax(energy)
        value = self.value_fc(x)
        out = torch.bmm(attention, value)
        return out, attention


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


def get_init_inputs():
    return [[], {'in_dims': 4, 'sentence_length': 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 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_repeat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_1(in_ptr0, in_ptr1, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = triton_helpers.maximum(tmp2, tmp5)
    tmp9 = tmp7 * tmp8
    tmp10 = triton_helpers.maximum(tmp6, tmp9)
    tmp13 = tmp11 * tmp12
    tmp14 = triton_helpers.maximum(tmp10, tmp13)
    tmp15 = tmp2 - tmp14
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp5 - tmp14
    tmp18 = tl_math.exp(tmp17)
    tmp19 = tmp16 + tmp18
    tmp20 = tmp9 - tmp14
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp13 - tmp14
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tl.store(out_ptr0 + x0, tmp14, xmask)
    tl.store(out_ptr1 + x0, tmp25, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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_ptr1 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp7 = tmp5 / tmp6
    tl.store(out_ptr0 + x2, tmp7, 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,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    assert_size_stride(primals_5, (1,), (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.addmm(primals_2, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf2)
        del primals_4
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_repeat_0[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf2, (4, 4, 1), (4, 1, 16), 0)
        del buf2
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_mul_1[grid(16)](buf0, buf3, buf4, buf5, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mul_2[grid(64)](buf0, buf3, buf4, buf5,
            buf6, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf4
        del buf5
        buf7 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_3, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf7)
        del primals_6
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf7, (4, 4, 4), (16, 4,
            1), 0), out=buf8)
    return buf8, buf6, reinterpret_tensor(primals_3, (16, 4), (4, 1), 0
        ), buf0, buf3, buf6, reinterpret_tensor(buf7, (4, 4, 4), (16, 1, 4), 0)


class FactorizedSynthesizerDenseNew(nn.Module):

    def __init__(self, in_dims, sentence_length):
        super(FactorizedSynthesizerDenseNew, self).__init__()
        self.a = 4
        self.b = sentence_length // self.a
        self.a_proj = nn.Linear(in_dims, self.a)
        self.b_proj = nn.Linear(in_dims, self.b)
        self.value_fc = nn.Linear(in_dims, in_dims)
        self.softmax = nn.Softmax(dim=-1)

    def forward(self, input_0):
        primals_1 = self.a_proj.weight
        primals_2 = self.a_proj.bias
        primals_4 = self.b_proj.weight
        primals_5 = self.b_proj.bias
        primals_6 = self.value_fc.weight
        primals_7 = self.value_fc.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
