#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# TODO: import all neural network related api under this directory,
# including layers, linear, conv, rnn etc.

from .activation import celu  # noqa: F401
from .activation import elu  # noqa: F401
from .activation import elu_  # noqa: F401
from .activation import gelu  # noqa: F401
from .activation import hardshrink  # noqa: F401
from .activation import hardtanh  # noqa: F401
from .activation import hardsigmoid  # noqa: F401
from .activation import hardswish  # noqa: F401
from .activation import leaky_relu  # noqa: F401
from .activation import log_sigmoid  # noqa: F401
from .activation import maxout  # noqa: F401
from .activation import prelu  # noqa: F401
from .activation import relu  # noqa: F401
from .activation import relu_  # noqa: F401
from .activation import relu6  # noqa: F401
from .activation import selu  # noqa: F401
from .activation import sigmoid  # noqa: F401
from .activation import silu  # noqa: F401
from .activation import softmax  # noqa: F401
from .activation import softmax_  # noqa: F401
from .activation import softplus  # noqa: F401
from .activation import softshrink  # noqa: F401
from .activation import softsign  # noqa: F401
from .activation import swish  # noqa: F401
from .activation import mish  # noqa: F401
from .activation import tanh  # noqa: F401
from .activation import tanh_  # noqa: F401
from .activation import tanhshrink  # noqa: F401
from .activation import thresholded_relu  # noqa: F401
from .activation import log_softmax  # noqa: F401
from .activation import glu  # noqa: F401
from .activation import gumbel_softmax  # noqa: F401
from .activation import rrelu  # noqa: F401
from .common import dropout  # noqa: F401
from .common import dropout2d  # noqa: F401
from .common import dropout3d  # noqa: F401
from .common import alpha_dropout  # noqa: F401
from .common import label_smooth  # noqa: F401
from .common import pad  # noqa: F401
from .common import zeropad2d  # noqa: F401
from .common import cosine_similarity  # noqa: F401
from .common import unfold  # noqa: F401
from .common import fold
from .common import interpolate  # noqa: F401
from .common import upsample  # noqa: F401
from .common import bilinear  # noqa: F401
from .common import class_center_sample  # noqa: F401
from .conv import conv1d  # noqa: F401
from .conv import conv1d_transpose  # noqa: F401
from .common import linear  # noqa: F401
from .conv import conv2d  # noqa: F401
from .conv import conv2d_transpose  # noqa: F401
from .conv import conv3d  # noqa: F401
from .conv import conv3d_transpose  # noqa: F401
from .distance import pairwise_distance  # noqa: F401
from .extension import diag_embed  # noqa: F401
from .extension import sequence_mask
from .loss import binary_cross_entropy  # noqa: F401
from .loss import binary_cross_entropy_with_logits  # noqa: F401
from .loss import cross_entropy  # noqa: F401
from .loss import dice_loss  # noqa: F401
from .loss import hsigmoid_loss  # noqa: F401
from .loss import kl_div  # noqa: F401
from .loss import l1_loss  # noqa: F401
from .loss import log_loss  # noqa: F401
from .loss import margin_ranking_loss  # noqa: F401
from .loss import mse_loss  # noqa: F401
from .loss import nll_loss  # noqa: F401
from .loss import poisson_nll_loss  # noqa: F401
from .loss import npair_loss  # noqa: F401
from .loss import sigmoid_focal_loss  # noqa: F401
from .loss import smooth_l1_loss  # noqa: F401
from .loss import softmax_with_cross_entropy  # noqa: F401
from .loss import margin_cross_entropy  # noqa: F401
from .loss import square_error_cost  # noqa: F401
from .loss import ctc_loss  # noqa: F401
from .loss import rnnt_loss  # noqa: F401
from .loss import hinge_embedding_loss  # noqa: F401
from .loss import cosine_embedding_loss  # noqa: F401
from .loss import multi_margin_loss
from .loss import multi_label_soft_margin_loss
from .loss import triplet_margin_with_distance_loss
from .loss import triplet_margin_loss
from .loss import soft_margin_loss
from .norm import batch_norm  # noqa: F401
from .norm import instance_norm  # noqa: F401
from .norm import layer_norm  # noqa: F401
from .norm import local_response_norm  # noqa: F401
from .norm import normalize  # noqa: F401
from .pooling import avg_pool1d  # noqa: F401
from .pooling import avg_pool2d  # noqa: F401
from .pooling import avg_pool3d  # noqa: F401
from .pooling import max_pool1d  # noqa: F401
from .pooling import max_pool2d  # noqa: F401
from .pooling import max_pool3d  # noqa: F401

from .pooling import adaptive_max_pool1d  # noqa: F401
from .pooling import adaptive_max_pool2d  # noqa: F401
from .pooling import adaptive_max_pool3d  # noqa: F401
from .pooling import adaptive_avg_pool1d  # noqa: F401
from .pooling import adaptive_avg_pool2d  # noqa: F401
from .pooling import adaptive_avg_pool3d  # noqa: F401
from .pooling import max_unpool1d  # noqa: F401
from .pooling import max_unpool2d  # noqa: F401
from .pooling import max_unpool3d  # noqa: F401

from .vision import affine_grid  # noqa: F401
from .vision import grid_sample  # noqa: F401
from .vision import pixel_shuffle  # noqa: F401
from .vision import pixel_unshuffle  # noqa: F401
from .vision import channel_shuffle  # noqa: F401
from .input import one_hot  # noqa: F401
from .input import embedding  # noqa: F401
from .extension import gather_tree  # noqa: F401
from .extension import temporal_shift  # noqa: F401

from .sparse_attention import sparse_attention

__all__ = [  # noqa
    'celu',
    'conv1d',
    'conv1d_transpose',
    'conv2d',
    'conv2d_transpose',
    'conv3d',
    'conv3d_transpose',
    'pairwise_distance',
    'elu',
    'elu_',
    'gelu',
    'hardshrink',
    'hardtanh',
    'hardsigmoid',
    'hardswish',
    'leaky_relu',
    'log_sigmoid',
    'maxout',
    'prelu',
    'relu',
    'relu_',
    'relu6',
    'selu',
    'softmax',
    'softmax_',
    'softplus',
    'softshrink',
    'softsign',
    'sigmoid',
    'silu',
    'swish',
    'mish',
    'tanh',
    'tanh_',
    'tanhshrink',
    'thresholded_relu',
    'log_softmax',
    'glu',
    'gumbel_softmax',
    'diag_embed',
    'sequence_mask',
    'dropout',
    'dropout2d',
    'dropout3d',
    'alpha_dropout',
    'label_smooth',
    'linear',
    'pad',
    'zeropad2d',
    'unfold',
    'interpolate',
    'upsample',
    'bilinear',
    'cosine_similarity',
    'avg_pool1d',
    'avg_pool2d',
    'avg_pool3d',
    'max_pool1d',
    'max_pool2d',
    'max_pool3d',
    'max_unpool1d',
    'max_unpool2d',
    'max_unpool3d',
    'adaptive_avg_pool1d',
    'adaptive_avg_pool2d',
    'adaptive_avg_pool3d',
    'adaptive_max_pool1d',
    'adaptive_max_pool2d',
    'adaptive_max_pool3d',
    'binary_cross_entropy',
    'binary_cross_entropy_with_logits',
    'cross_entropy',
    'dice_loss',
    'hsigmoid_loss',
    'kl_div',
    'l1_loss',
    'log_loss',
    'mse_loss',
    'margin_ranking_loss',
    'multi_label_soft_margin_loss',
    'nll_loss',
    'poisson_nll_loss',
    'npair_loss',
    'sigmoid_focal_loss',
    'smooth_l1_loss',
    'softmax_with_cross_entropy',
    'margin_cross_entropy',
    'square_error_cost',
    'ctc_loss',
    'rnnt_loss',
    'hinge_embedding_loss',
    'affine_grid',
    'grid_sample',
    'local_response_norm',
    'pixel_shuffle',
    'pixel_unshuffle',
    'channel_shuffle',
    'embedding',
    'gather_tree',
    'one_hot',
    'normalize',
    'temporal_shift',
    'batch_norm',
    'layer_norm',
    'instance_norm',
    'class_center_sample',
    'sparse_attention',
    'fold',
    'cosine_embedding_loss',
    'rrelu',
    'triplet_margin_with_distance_loss',
    'triplet_margin_loss',
    'multi_margin_loss',
    'soft_margin_loss',
]