# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
# Copyright 2019 <NAME>
# 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.

import torch


class PositionwiseFeedForward(torch.nn.Module):
    """Positionwise feed forward layer.

    Args:
        idim (int): Input dimenstion.
        hidden_units (int): The number of hidden units.
        dropout_rate (float): Dropout rate.

    """

    def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
        """Construct an PositionwiseFeedForward object."""
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = torch.nn.Linear(idim, hidden_units)
        self.w_2 = torch.nn.Linear(hidden_units, idim)
        self.dropout = torch.nn.Dropout(dropout_rate, inplace=True)
        self.activation = activation

    def forward(self, x):
        """Forward function."""
        return self.w_2(self.dropout(self.activation(self.w_1(x))))

# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
# Copyright 2019 <NAME>
# 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.

import torch


class PositionwiseFeedForward(torch.nn.Module):
    """Positionwise feed forward layer.

    Args:
        idim (int): Input dimenstion.
        hidden_units (int): The number of hidden units.
        dropout_rate (float): Dropout rate.

    """

    def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()):
        """Construct an PositionwiseFeedForward object."""
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = torch.nn.Linear(idim, hidden_units)
        self.w_2 = torch.nn.Linear(hidden_units, idim)
        self.dropout = torch.nn.Dropout(dropout_rate, inplace=True)
        self.activation = activation

    def forward(self, x):
        """Forward function."""
        return self.w_2(self.dropout(self.activation(self.w_1(x))))

# Copyright (c) 2021 Graphcore Ltd. All rights reserved. # Copyright 2019 <NAME> # 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. Positionwise feed forward layer.      Args:         idim (int): Input dimenstion.         hidden_units (int): The number of hidden units.         dropout_rate (float): Dropout rate. Construct an PositionwiseFeedForward object. Forward function.