from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel


def gelu(x):
    """Implementation of the gelu activation function.
        For information: OpenAI GPT"s gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
        Also see https://arxiv.org/abs/1606.08415
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


class BertLayerNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-12):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(BertLayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias


class BertPredictionHeadTransform(nn.Module):

    def __init__(self, config):
        super(BertPredictionHeadTransform, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.transform_act_fn = gelu
        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)

    def forward(self, hidden_states):
        """(N, L, D)"""
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertPredictionHead(nn.Module):

    def __init__(self, config, output_size):
        super(BertPredictionHead, self).__init__()
        self.transform = BertPredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, output_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(output_size))

    def forward(self, hidden_states):
        """(N, L, D)"""
        hidden_states = self.transform(hidden_states)
        hidden_states = torch.nn.functional.relu(self.decoder(hidden_states
            ) + self.bias)
        return hidden_states


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


def get_init_inputs():
    return [[], {'config': _mock_config(hidden_size=4, layer_norm_eps=1),
        'output_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
import math
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
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_div_erf_mean_mul_pow_sub_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 = 0.7071067811865475
    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_add_div_erf_mul_sqrt_sub_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
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp10 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = 0.7071067811865475
    tmp5 = tmp1 * tmp4
    tmp6 = libdevice.erf(tmp5)
    tmp7 = 1.0
    tmp8 = tmp6 + tmp7
    tmp9 = tmp3 * tmp8
    tmp11 = tmp9 - tmp10
    tmp13 = tmp12 + tmp7
    tmp14 = libdevice.sqrt(tmp13)
    tmp15 = tmp11 / tmp14
    tmp16 = tmp0 * tmp15
    tmp18 = tmp16 + tmp17
    tl.store(out_ptr0 + x2, tmp18, xmask)


@triton.jit
def triton_poi_fused_add_relu_threshold_backward_2(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)


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_add_div_erf_mean_mul_pow_sub_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_add_div_erf_mul_sqrt_sub_1[grid(256)](primals_4,
            buf0, buf1, buf2, primals_5, buf3, 256, XBLOCK=128, 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.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf4)
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_add_relu_threshold_backward_2[grid(256)](buf5,
            primals_7, buf6, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
    return buf5, primals_4, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf3, (64, 4), (4, 1), 0), buf6, primals_6


def gelu(x):
    """Implementation of the gelu activation function.
        For information: OpenAI GPT"s gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
        Also see https://arxiv.org/abs/1606.08415
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))


class BertLayerNorm(nn.Module):

    def __init__(self, hidden_size, eps=1e-12):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(BertLayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias


class BertPredictionHeadTransform(nn.Module):

    def __init__(self, config):
        super(BertPredictionHeadTransform, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.transform_act_fn = gelu
        self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.
            layer_norm_eps)

    def forward(self, hidden_states):
        """(N, L, D)"""
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class BertPredictionHeadNew(nn.Module):

    def __init__(self, config, output_size):
        super(BertPredictionHeadNew, self).__init__()
        self.transform = BertPredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, output_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(output_size))

    def forward(self, input_0):
        primals_2 = self.bias
        primals_1 = self.transform.dense.weight
        primals_4 = self.transform.dense.bias
        primals_5 = self.transform.LayerNorm.weight
        primals_7 = self.transform.LayerNorm.bias
        primals_6 = self.decoder.weight
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
