# Copyright (c) 2020, <NAME>. 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.

import torch
import torch.nn as nn
from torch import Tensor
from typing import Tuple


class MaskCNN(nn.Module):
    r"""
    Masking Convolutional Neural Network

    Adds padding to the output of the module based on the given lengths.
    This is to ensure that the results of the model do not change when batch sizes change during inference.
    Input needs to be in the shape of (batch_size, channel, hidden_dim, seq_len)

    Refer to https://github.com/SeanNaren/deepspeech.pytorch/blob/master/model.py
    Copyright (c) 2017 <NAME>
    MIT License

    Args:
        sequential (torch.nn): sequential list of convolution layer

    Inputs: inputs, seq_lengths
        - **inputs** (torch.FloatTensor): The input of size BxCxHxT
        - **seq_lengths** (torch.IntTensor): The actual length of each sequence in the batch

    Returns: output, seq_lengths
        - **output**: Masked output from the sequential
        - **seq_lengths**: Sequence length of output from the sequential
    """
    def __init__(self, sequential: nn.Sequential) -> None:
        super(MaskCNN, self).__init__()
        self.sequential = sequential

    def forward(self, inputs: Tensor, seq_lengths: Tensor) -> Tuple[Tensor, Tensor]:
        output = None

        for module in self.sequential:
            output = module(inputs)
            mask = torch.BoolTensor(output.size()).fill_(0)

            if output.is_cuda:
                mask = mask.cuda()

            seq_lengths = self._get_sequence_lengths(module, seq_lengths)

            for idx, length in enumerate(seq_lengths):
                length = length.item()

                if (mask[idx].size(2) - length) > 0:
                    mask[idx].narrow(dim=2, start=length, length=mask[idx].size(2) - length).fill_(1)

            output = output.masked_fill(mask, 0)
            inputs = output

        return output, seq_lengths

    def _get_sequence_lengths(self, module: nn.Module, seq_lengths: Tensor) -> Tensor:
        r"""
        Calculate convolutional neural network receptive formula

        Args:
            module (torch.nn.Module): module of CNN
            seq_lengths (torch.IntTensor): The actual length of each sequence in the batch

        Returns: seq_lengths
            - **seq_lengths**: Sequence length of output from the module
        """
        if isinstance(module, nn.Conv2d):
            numerator = seq_lengths + 2 * module.padding[1] - module.dilation[1] * (module.kernel_size[1] - 1) - 1
            seq_lengths = numerator.float() / float(module.stride[1])
            seq_lengths = seq_lengths.int() + 1

        elif isinstance(module, nn.MaxPool2d):
            seq_lengths >>= 1

        return seq_lengths.int()


class VGGExtractor(nn.Module):
    r"""
    VGG extractor for automatic speech recognition described in
    "Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM" paper
    - https://arxiv.org/pdf/1706.02737.pdf

    Args:
        input_dim (int): Dimension of input vector
        in_channels (int): Number of channels in the input image
        out_channels (int or tuple): Number of channels produced by the convolution

    Inputs: inputs, input_lengths
        - **inputs** (batch, time, dim): Tensor containing input vectors
        - **input_lengths**: Tensor containing containing sequence lengths

    Returns: outputs, output_lengths
        - **outputs**: Tensor produced by the convolution
        - **output_lengths**: Tensor containing sequence lengths produced by the convolution
    """
    def __init__(
            self,
            input_dim: int,
            in_channels: int = 1,
            out_channels: int or tuple = (64, 128),
    ):
        super(VGGExtractor, self).__init__()
        self.input_dim = input_dim
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.conv = MaskCNN(
            nn.Sequential(
                nn.Conv2d(in_channels, out_channels[0], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[0]),
                nn.ReLU(),
                nn.Conv2d(out_channels[0], out_channels[0], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[0]),
                nn.ReLU(),
                nn.MaxPool2d(2, stride=2),
                nn.Conv2d(out_channels[0], out_channels[1], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[1]),
                nn.ReLU(),
                nn.Conv2d(out_channels[1], out_channels[1], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[1]),
                nn.ReLU(),
                nn.MaxPool2d(2, stride=2),
            )
        )

    def get_output_lengths(self, seq_lengths: Tensor):
        assert self.conv is not None, "self.conv should be defined"

        for module in self.conv:
            if isinstance(module, nn.Conv2d):
                numerator = seq_lengths + 2 * module.padding[1] - module.dilation[1] * (module.kernel_size[1] - 1) - 1
                seq_lengths = numerator.float() / float(module.stride[1])
                seq_lengths = seq_lengths.int() + 1

            elif isinstance(module, nn.MaxPool2d):
                seq_lengths >>= 1

        return seq_lengths.int()

    def get_output_dim(self):
        return (self.input_dim - 1) << 5 if self.input_dim % 2 else self.input_dim << 5

    def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]:
        r"""
        inputs: torch.FloatTensor (batch, time, dimension)
        input_lengths: torch.IntTensor (batch)
        """
        outputs, output_lengths = self.conv(inputs.unsqueeze(1).transpose(2, 3), input_lengths)

        batch_size, channels, dimension, seq_lengths = outputs.size()
        outputs = outputs.permute(0, 3, 1, 2)
        outputs = outputs.view(batch_size, seq_lengths, channels * dimension)

        return outputs, output_lengths

# Copyright (c) 2020, <NAME>. 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.

import torch
import torch.nn as nn
from torch import Tensor
from typing import Tuple


class MaskCNN(nn.Module):
    r"""
    Masking Convolutional Neural Network

    Adds padding to the output of the module based on the given lengths.
    This is to ensure that the results of the model do not change when batch sizes change during inference.
    Input needs to be in the shape of (batch_size, channel, hidden_dim, seq_len)

    Refer to https://github.com/SeanNaren/deepspeech.pytorch/blob/master/model.py
    Copyright (c) 2017 <NAME>
    MIT License

    Args:
        sequential (torch.nn): sequential list of convolution layer

    Inputs: inputs, seq_lengths
        - **inputs** (torch.FloatTensor): The input of size BxCxHxT
        - **seq_lengths** (torch.IntTensor): The actual length of each sequence in the batch

    Returns: output, seq_lengths
        - **output**: Masked output from the sequential
        - **seq_lengths**: Sequence length of output from the sequential
    """
    def __init__(self, sequential: nn.Sequential) -> None:
        super(MaskCNN, self).__init__()
        self.sequential = sequential

    def forward(self, inputs: Tensor, seq_lengths: Tensor) -> Tuple[Tensor, Tensor]:
        output = None

        for module in self.sequential:
            output = module(inputs)
            mask = torch.BoolTensor(output.size()).fill_(0)

            if output.is_cuda:
                mask = mask.cuda()

            seq_lengths = self._get_sequence_lengths(module, seq_lengths)

            for idx, length in enumerate(seq_lengths):
                length = length.item()

                if (mask[idx].size(2) - length) > 0:
                    mask[idx].narrow(dim=2, start=length, length=mask[idx].size(2) - length).fill_(1)

            output = output.masked_fill(mask, 0)
            inputs = output

        return output, seq_lengths

    def _get_sequence_lengths(self, module: nn.Module, seq_lengths: Tensor) -> Tensor:
        r"""
        Calculate convolutional neural network receptive formula

        Args:
            module (torch.nn.Module): module of CNN
            seq_lengths (torch.IntTensor): The actual length of each sequence in the batch

        Returns: seq_lengths
            - **seq_lengths**: Sequence length of output from the module
        """
        if isinstance(module, nn.Conv2d):
            numerator = seq_lengths + 2 * module.padding[1] - module.dilation[1] * (module.kernel_size[1] - 1) - 1
            seq_lengths = numerator.float() / float(module.stride[1])
            seq_lengths = seq_lengths.int() + 1

        elif isinstance(module, nn.MaxPool2d):
            seq_lengths >>= 1

        return seq_lengths.int()


class VGGExtractor(nn.Module):
    r"""
    VGG extractor for automatic speech recognition described in
    "Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM" paper
    - https://arxiv.org/pdf/1706.02737.pdf

    Args:
        input_dim (int): Dimension of input vector
        in_channels (int): Number of channels in the input image
        out_channels (int or tuple): Number of channels produced by the convolution

    Inputs: inputs, input_lengths
        - **inputs** (batch, time, dim): Tensor containing input vectors
        - **input_lengths**: Tensor containing containing sequence lengths

    Returns: outputs, output_lengths
        - **outputs**: Tensor produced by the convolution
        - **output_lengths**: Tensor containing sequence lengths produced by the convolution
    """
    def __init__(
            self,
            input_dim: int,
            in_channels: int = 1,
            out_channels: int or tuple = (64, 128),
    ):
        super(VGGExtractor, self).__init__()
        self.input_dim = input_dim
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.conv = MaskCNN(
            nn.Sequential(
                nn.Conv2d(in_channels, out_channels[0], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[0]),
                nn.ReLU(),
                nn.Conv2d(out_channels[0], out_channels[0], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[0]),
                nn.ReLU(),
                nn.MaxPool2d(2, stride=2),
                nn.Conv2d(out_channels[0], out_channels[1], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[1]),
                nn.ReLU(),
                nn.Conv2d(out_channels[1], out_channels[1], kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(num_features=out_channels[1]),
                nn.ReLU(),
                nn.MaxPool2d(2, stride=2),
            )
        )

    def get_output_lengths(self, seq_lengths: Tensor):
        assert self.conv is not None, "self.conv should be defined"

        for module in self.conv:
            if isinstance(module, nn.Conv2d):
                numerator = seq_lengths + 2 * module.padding[1] - module.dilation[1] * (module.kernel_size[1] - 1) - 1
                seq_lengths = numerator.float() / float(module.stride[1])
                seq_lengths = seq_lengths.int() + 1

            elif isinstance(module, nn.MaxPool2d):
                seq_lengths >>= 1

        return seq_lengths.int()

    def get_output_dim(self):
        return (self.input_dim - 1) << 5 if self.input_dim % 2 else self.input_dim << 5

    def forward(self, inputs: Tensor, input_lengths: Tensor) -> Tuple[Tensor, Tensor]:
        r"""
        inputs: torch.FloatTensor (batch, time, dimension)
        input_lengths: torch.IntTensor (batch)
        """
        outputs, output_lengths = self.conv(inputs.unsqueeze(1).transpose(2, 3), input_lengths)

        batch_size, channels, dimension, seq_lengths = outputs.size()
        outputs = outputs.permute(0, 3, 1, 2)
        outputs = outputs.view(batch_size, seq_lengths, channels * dimension)

        return outputs, output_lengths

# Copyright (c) 2020, <NAME>. 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. Masking Convolutional Neural Network      Adds padding to the output of the module based on the given lengths.     This is to ensure that the results of the model do not change when batch sizes change during inference.     Input needs to be in the shape of (batch_size, channel, hidden_dim, seq_len)      Refer to https://github.com/SeanNaren/deepspeech.pytorch/blob/master/model.py     Copyright (c) 2017 <NAME>     MIT License      Args:         sequential (torch.nn): sequential list of convolution layer      Inputs: inputs, seq_lengths         - **inputs** (torch.FloatTensor): The input of size BxCxHxT         - **seq_lengths** (torch.IntTensor): The actual length of each sequence in the batch      Returns: output, seq_lengths         - **output**: Masked output from the sequential         - **seq_lengths**: Sequence length of output from the sequential Calculate convolutional neural network receptive formula          Args:             module (torch.nn.Module): module of CNN             seq_lengths (torch.IntTensor): The actual length of each sequence in the batch          Returns: seq_lengths             - **seq_lengths**: Sequence length of output from the module VGG extractor for automatic speech recognition described in     "Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM" paper     - https://arxiv.org/pdf/1706.02737.pdf      Args:         input_dim (int): Dimension of input vector         in_channels (int): Number of channels in the input image         out_channels (int or tuple): Number of channels produced by the convolution      Inputs: inputs, input_lengths         - **inputs** (batch, time, dim): Tensor containing input vectors         - **input_lengths**: Tensor containing containing sequence lengths      Returns: outputs, output_lengths         - **outputs**: Tensor produced by the convolution         - **output_lengths**: Tensor containing sequence lengths produced by the convolution inputs: torch.FloatTensor (batch, time, dimension)         input_lengths: torch.IntTensor (batch)