import torch
from torch import nn


class FeedForward(nn.Module):

    def __init__(self, dhidden, dropout_rate, **kwargs):
        super(FeedForward, self).__init__(**kwargs)
        self.dhidden = dhidden
        self.dropout_rate = dropout_rate
        self.dense_1 = nn.Linear(dhidden, 4 * dhidden)
        self.dense_2 = nn.Linear(4 * dhidden, dhidden)
        self.gelu = nn.GELU()
        self.dropout = nn.Dropout(dropout_rate)

    def get_config(self):
        config = super(FeedForward, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, x):
        x = self.dense_1(x)
        x = self.gelu(x)
        x = self.dropout(x)
        x = self.dense_2(x)
        x = self.dropout(x)
        return x


class FNetEncoder(nn.Module):

    def __init__(self, dhidden=512, dropout_rate=0):
        super(FNetEncoder, self).__init__()
        self.feedforward = FeedForward(dhidden, dropout_rate)
        self.LayerNorm_1 = nn.LayerNorm(dhidden)
        self.LayerNorm_2 = nn.LayerNorm(dhidden)

    def get_config(self):
        config = super(FNetEncoder, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, inputs):
        x_fft = torch.real(torch.fft.fft(torch.fft.fft(inputs).T).T)
        x = self.LayerNorm_1(inputs + x_fft)
        x_ff = self.feedforward(x)
        x = self.LayerNorm_2(x + x_ff)
        return x


def get_inputs():
    return [torch.rand([4, 4, 512, 512])]


def get_init_inputs():
    return [[], {}]

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
from torch import 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_red_fused_add_native_layer_norm_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr2, out_ptr3, xnumel, rnumel, XBLOCK: tl.constexpr,
    RBLOCK: tl.constexpr):
    rnumel = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    x3 = xindex
    x0 = xindex % 2048
    x1 = xindex // 2048
    tmp4_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp4_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp0 = tl.load(in_ptr0 + (r2 + 512 * x3), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = tl.load(in_ptr1 + (2 * x1 + 8 * r2 + 4096 * x0), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp2 = tmp0 + tmp1
        tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
        tmp4_mean_next, tmp4_m2_next, tmp4_weight_next = (triton_helpers.
            welford_reduce(tmp3, tmp4_mean, tmp4_m2, tmp4_weight, roffset == 0)
            )
        tmp4_mean = tl.where(rmask, tmp4_mean_next, tmp4_mean)
        tmp4_m2 = tl.where(rmask, tmp4_m2_next, tmp4_m2)
        tmp4_weight = tl.where(rmask, tmp4_weight_next, tmp4_weight)
    tmp4_tmp, tmp5_tmp, tmp6_tmp = triton_helpers.welford(tmp4_mean,
        tmp4_m2, tmp4_weight, 1)
    tmp4 = tmp4_tmp[:, None]
    tmp5 = tmp5_tmp[:, None]
    tmp6_tmp[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp7 = tl.load(in_ptr0 + (r2 + 512 * x3), rmask, eviction_policy=
            'evict_first', other=0.0)
        tmp8 = tl.load(in_ptr1 + (2 * x1 + 8 * r2 + 4096 * x0), rmask,
            eviction_policy='evict_last', other=0.0)
        tmp17 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp19 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp9 = tmp7 + tmp8
        tmp10 = tmp9 - tmp4
        tmp11 = 512.0
        tmp12 = tmp5 / tmp11
        tmp13 = 1e-05
        tmp14 = tmp12 + tmp13
        tmp15 = libdevice.rsqrt(tmp14)
        tmp16 = tmp10 * tmp15
        tmp18 = tmp16 * tmp17
        tmp20 = tmp18 + tmp19
        tl.store(out_ptr2 + (r2 + 512 * x3), tmp16, rmask)
        tl.store(out_ptr3 + (r2 + 512 * x3), tmp20, rmask)


@triton.jit
def triton_poi_fused_gelu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    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
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2(in_ptr0
    , in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr2, out_ptr3, out_ptr4,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 512 * x0), None)
    tmp1 = tl.load(in_ptr1 + (r1 + 512 * x0), None)
    tmp2 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + r1, None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr4 + r1, None, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = tl.broadcast_to(tmp5, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = tl.full([1], 512, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp5 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp17 = triton_helpers.promote_to_tensor(tl.sum(tmp15, 0))
    tmp18 = tmp4 - tmp12
    tmp19 = 512.0
    tmp20 = tmp17 / tmp19
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tmp24 = tmp18 * tmp23
    tmp26 = tmp24 * tmp25
    tmp28 = tmp26 + tmp27
    tmp29 = 0.001953125
    tmp30 = tmp23 * tmp29
    tl.store(out_ptr2 + (r1 + 512 * x0), tmp24, None)
    tl.store(out_ptr3 + (r1 + 512 * x0), tmp28, None)
    tl.store(out_ptr4 + x0, tmp30, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 512, 512), (1048576, 262144, 512, 1))
    assert_size_stride(primals_2, (512,), (1,))
    assert_size_stride(primals_3, (512,), (1,))
    assert_size_stride(primals_4, (2048, 512), (512, 1))
    assert_size_stride(primals_5, (2048,), (1,))
    assert_size_stride(primals_6, (512, 2048), (2048, 1))
    assert_size_stride(primals_7, (512,), (1,))
    assert_size_stride(primals_8, (512,), (1,))
    assert_size_stride(primals_9, (512,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten._fft_r2c.default(primals_1, [3], 0, False)
        buf1 = buf0
        del buf0
        buf2 = torch.ops.aten.permute.default(buf1, [3, 2, 1, 0])
        buf3 = buf2
        buf4 = torch.ops.aten._fft_c2c.default(buf3, [3], 0, True)
        del buf1
        del buf2
        del buf3
        buf5 = buf4
        del buf4
        buf6 = torch.ops.aten.permute.default(buf5, [3, 2, 1, 0])
        buf7 = buf6
        buf8 = torch.ops.aten.view_as_real.default(buf7)
        buf9 = buf8
        buf13 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        buf14 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        get_raw_stream(0)
        triton_red_fused_add_native_layer_norm_0[grid(8192)](primals_1,
            buf9, primals_2, primals_3, buf13, buf14, 8192, 512, XBLOCK=8,
            RBLOCK=512, num_warps=16, num_stages=1)
        del buf5
        del buf6
        del buf7
        del buf8
        del buf9
        del primals_1
        del primals_2
        del primals_3
        buf15 = empty_strided_cuda((8192, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf14, (8192, 
            512), (512, 1), 0), reinterpret_tensor(primals_4, (512, 2048),
            (1, 512), 0), alpha=1, beta=1, out=buf15)
        del primals_5
        buf16 = empty_strided_cuda((4, 4, 512, 2048), (4194304, 1048576, 
            2048, 1), torch.float32)
        triton_poi_fused_gelu_1[grid(16777216)](buf15, buf16, 16777216,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf17 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf16, (8192, 2048), (2048, 1),
            0), reinterpret_tensor(primals_6, (2048, 512), (1, 2048), 0),
            out=buf17)
        buf21 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        buf22 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch
            .float32)
        triton_per_fused_add_native_layer_norm_native_layer_norm_backward_2[
            grid(8192)](buf14, buf17, primals_7, primals_8, primals_9,
            buf21, buf22, buf23, 8192, 512, num_warps=4, num_stages=1)
        del buf17
        del primals_7
        del primals_9
    return buf22, primals_8, buf13, reinterpret_tensor(buf14, (8192, 512),
        (512, 1), 0), buf15, reinterpret_tensor(buf16, (8192, 2048), (2048,
        1), 0), buf21, buf23, primals_6, primals_4


class FeedForward(nn.Module):

    def __init__(self, dhidden, dropout_rate, **kwargs):
        super(FeedForward, self).__init__(**kwargs)
        self.dhidden = dhidden
        self.dropout_rate = dropout_rate
        self.dense_1 = nn.Linear(dhidden, 4 * dhidden)
        self.dense_2 = nn.Linear(4 * dhidden, dhidden)
        self.gelu = nn.GELU()
        self.dropout = nn.Dropout(dropout_rate)

    def get_config(self):
        config = super(FeedForward, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, x):
        x = self.dense_1(x)
        x = self.gelu(x)
        x = self.dropout(x)
        x = self.dense_2(x)
        x = self.dropout(x)
        return x


class FNetEncoderNew(nn.Module):

    def __init__(self, dhidden=512, dropout_rate=0):
        super(FNetEncoderNew, self).__init__()
        self.feedforward = FeedForward(dhidden, dropout_rate)
        self.LayerNorm_1 = nn.LayerNorm(dhidden)
        self.LayerNorm_2 = nn.LayerNorm(dhidden)

    def get_config(self):
        config = super(FNetEncoderNew, self).get_config()
        config.update({'dhidden': self.dhidden, 'dropout_rate': self.
            dropout_rate})
        return config

    def forward(self, input_0):
        primals_4 = self.feedforward.dense_1.weight
        primals_5 = self.feedforward.dense_1.bias
        primals_6 = self.feedforward.dense_2.weight
        primals_2 = self.feedforward.dense_2.bias
        primals_3 = self.LayerNorm_1.weight
        primals_7 = self.LayerNorm_1.bias
        primals_8 = self.LayerNorm_2.weight
        primals_9 = self.LayerNorm_2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]
