# Copyright (c) 2021 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.
from collections import OrderedDict

import paddle
from paddle import nn
from paddle.nn import functional as F

from deepspeech.utils.log import Log

logger = Log(__name__).getlog()

__all__ = ["get_activation", "brelu", "LinearGLUBlock", "ConvGLUBlock", "GLU"]


def brelu(x, t_min=0.0, t_max=24.0, name=None):
    # paddle.to_tensor is dygraph_only can not work under JIT
    t_min = paddle.full(shape=[1], fill_value=t_min, dtype='float32')
    t_max = paddle.full(shape=[1], fill_value=t_max, dtype='float32')
    return x.maximum(t_min).minimum(t_max)


class GLU(nn.Layer):
    """Gated Linear Units (GLU) Layer"""

    def __init__(self, dim: int=-1):
        super().__init__()
        self.dim = dim

    def forward(self, xs):
        return F.glu(xs, axis=self.dim)


class LinearGLUBlock(nn.Layer):
    """A linear Gated Linear Units (GLU) block."""

    def __init__(self, idim: int):
        """ GLU.
        Args:
            idim (int): input and output dimension
        """
        super().__init__()
        self.fc = nn.Linear(idim, idim * 2)

    def forward(self, xs):
        return glu(self.fc(xs), dim=-1)


class ConvGLUBlock(nn.Layer):
    def __init__(self, kernel_size, in_ch, out_ch, bottlececk_dim=0,
                 dropout=0.):
        """A convolutional Gated Linear Units (GLU) block.

        Args:
            kernel_size (int): kernel size
            in_ch (int): number of input channels
            out_ch (int): number of output channels
            bottlececk_dim (int): dimension of the bottleneck layers for computational efficiency. Defaults to 0.
            dropout (float): dropout probability. Defaults to 0..
        """

        super().__init__()

        self.conv_residual = None
        if in_ch != out_ch:
            self.conv_residual = nn.utils.weight_norm(
                nn.Conv2D(
                    in_channels=in_ch, out_channels=out_ch, kernel_size=(1, 1)),
                name='weight',
                dim=0)
            self.dropout_residual = nn.Dropout(p=dropout)

        self.pad_left = nn.Pad2d((0, 0, kernel_size - 1, 0), 0)

        layers = OrderedDict()
        if bottlececk_dim == 0:
            layers['conv'] = nn.utils.weight_norm(
                nn.Conv2D(
                    in_channels=in_ch,
                    out_channels=out_ch * 2,
                    kernel_size=(kernel_size, 1)),
                name='weight',
                dim=0)
            # TODO(hirofumi0810): padding?
            layers['dropout'] = nn.Dropout(p=dropout)
            layers['glu'] = GLU()

        elif bottlececk_dim > 0:
            layers['conv_in'] = nn.utils.weight_norm(
                nn.Conv2D(
                    in_channels=in_ch,
                    out_channels=bottlececk_dim,
                    kernel_size=(1, 1)),
                name='weight',
                dim=0)
            layers['dropout_in'] = nn.Dropout(p=dropout)
            layers['conv_bottleneck'] = nn.utils.weight_norm(
                nn.Conv2D(
                    in_channels=bottlececk_dim,
                    out_channels=bottlececk_dim,
                    kernel_size=(kernel_size, 1)),
                name='weight',
                dim=0)
            layers['dropout'] = nn.Dropout(p=dropout)
            layers['glu'] = GLU()
            layers['conv_out'] = nn.utils.weight_norm(
                nn.Conv2D(
                    in_channels=bottlececk_dim,
                    out_channels=out_ch * 2,
                    kernel_size=(1, 1)),
                name='weight',
                dim=0)
            layers['dropout_out'] = nn.Dropout(p=dropout)

        self.layers = nn.Sequential(layers)

    def forward(self, xs):
        """Forward pass.
        Args:
            xs (FloatTensor): `[B, in_ch, T, feat_dim]`
        Returns:
            out (FloatTensor): `[B, out_ch, T, feat_dim]`
        """
        residual = xs
        if self.conv_residual is not None:
            residual = self.dropout_residual(self.conv_residual(residual))
        xs = self.pad_left(xs)  # `[B, embed_dim, T+kernel-1, 1]`
        xs = self.layers(xs)  # `[B, out_ch * 2, T ,1]`
        xs = xs + residual
        return xs


def get_activation(act):
    """Return activation function."""
    # Lazy load to avoid unused import
    activation_funcs = {
        "hardshrink": paddle.nn.Hardshrink,
        "hardswish": paddle.nn.Hardswish,
        "hardtanh": paddle.nn.Hardtanh,
        "tanh": paddle.nn.Tanh,
        "relu": paddle.nn.ReLU,
        "relu6": paddle.nn.ReLU6,
        "leakyrelu": paddle.nn.LeakyReLU,
        "selu": paddle.nn.SELU,
        "swish": paddle.nn.Swish,
        "gelu": paddle.nn.GELU,
        "glu": GLU,
        "elu": paddle.nn.ELU,
    }

    return activation_funcs[act]()