from _paritybench_helpers import _mock_config
from torch.nn import Module
import torch
from torch.nn import LayerNorm
from torch.nn import Linear
from torch.nn.functional import gelu


class BertLMHead(Module):

    def __init__(self, config):
        super(BertLMHead, self).__init__()
        hidden_size = config['hidden_size']
        self.mlp = Linear(hidden_size, hidden_size, bias=True)
        self.unembedding = Linear(hidden_size, config['vocab_size'], bias=True)
        self.layer_norm = LayerNorm((hidden_size,))

    def forward(self, activations):
        return self.unembedding(self.layer_norm(gelu(self.mlp(activations))))


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


def get_init_inputs():
    return [[], {'config': _mock_config(hidden_size=4, vocab_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.triton_helpers import libdevice
from torch.nn import Module
from torch.nn import LayerNorm
from torch.nn import Linear
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_gelu_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.erf(tmp11)
    tmp13 = tmp12 + tmp6
    tmp14 = tmp10 * tmp13
    tmp15 = tmp8 + tmp14
    tmp17 = tmp16 * tmp1
    tmp18 = tmp16 * tmp3
    tmp19 = libdevice.erf(tmp18)
    tmp20 = tmp19 + tmp6
    tmp21 = tmp17 * tmp20
    tmp22 = tmp15 + tmp21
    tmp24 = tmp23 * tmp1
    tmp25 = tmp23 * tmp3
    tmp26 = libdevice.erf(tmp25)
    tmp27 = tmp26 + tmp6
    tmp28 = tmp24 * tmp27
    tmp29 = tmp22 + tmp28
    tmp30 = 4.0
    tmp31 = tmp29 / tmp30
    tmp32 = tmp8 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp14 - tmp31
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp21 - tmp31
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp28 - tmp31
    tmp41 = tmp40 * tmp40
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42 / tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)
    tl.store(out_ptr1 + x0, tmp43, xmask)


@triton.jit
def triton_poi_fused_gelu_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tmp10 = tmp8 - tmp9
    tmp12 = 1e-05
    tmp13 = tmp11 + tmp12
    tmp14 = libdevice.rsqrt(tmp13)
    tmp15 = tmp10 * tmp14
    tmp17 = tmp15 * tmp16
    tmp19 = tmp17 + tmp18
    tl.store(out_ptr0 + x2, tmp19, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4,), (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((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            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
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_gelu_native_layer_norm_0[grid(64)](buf0, buf1,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_gelu_native_layer_norm_1[grid(256)](buf0, buf1,
            buf2, primals_4, primals_5, buf3, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf1
        del buf2
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf4)
        del primals_7
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), primals_6


class BertLMHeadNew(Module):

    def __init__(self, config):
        super(BertLMHeadNew, self).__init__()
        hidden_size = config['hidden_size']
        self.mlp = Linear(hidden_size, hidden_size, bias=True)
        self.unembedding = Linear(hidden_size, config['vocab_size'], bias=True)
        self.layer_norm = LayerNorm((hidden_size,))

    def forward(self, input_0):
        primals_1 = self.mlp.weight
        primals_2 = self.mlp.bias
        primals_6 = self.unembedding.weight
        primals_4 = self.unembedding.bias
        primals_5 = self.layer_norm.weight
        primals_7 = self.layer_norm.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
