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"""Sequence to sequence network with RNN cells."""
# from typing import Dict, Optional, Tuple
# from einops import rearrange
# from einops.layers.torch import Rearrange
# import torch
# from torch import nn
# from torch import Tensor
# from text_recognizer.networks.util import configure_backbone
# class Encoder(nn.Module):
# def __init__(
# self,
# embedding_dim: int,
# encoder_dim: int,
# decoder_dim: int,
# dropout_rate: float = 0.1,
# ) -> None:
# super().__init__()
# self.rnn = nn.GRU(
# input_size=embedding_dim, hidden_size=encoder_dim, bidirectional=True
# )
# self.fc = nn.Sequential(
# nn.Linear(in_features=2 * encoder_dim, out_features=decoder_dim), nn.Tanh()
# )
# self.dropout = nn.Dropout(p=dropout_rate)
# def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
# """Encodes a sequence of tensors with a bidirectional GRU.
# Args:
# x (Tensor): A input sequence.
# Shape:
# - x: :math:`(T, N, E)`.
# - output[0]: :math:`(T, N, 2 * E)`.
# - output[1]: :math:`(T, N, D)`.
# where T is the sequence length, N is the batch size, E is the
# embedding/encoder dimension, and D is the decoder dimension.
# Returns:
# Tuple[Tensor, Tensor]: The encoder output and the hidden state of the
# encoder.
# """
# output, hidden = self.rnn(x)
# # Get the hidden state from the forward and backward rnn.
# hidden_state = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)
# # Apply fully connected layer and tanh activation.
# hidden_state = self.fc(hidden_state)
# return output, hidden_state
# class Attention(nn.Module):
# def __init__(self, encoder_dim: int, decoder_dim: int) -> None:
# super().__init__()
# self.atten = nn.Linear(
# in_features=2 * encoder_dim + decoder_dim, out_features=decoder_dim
# )
# self.value = nn.Linear(in_features=decoder_dim, out_features=1, bias=False)
# def forward(self, hidden_state: Tensor, encoder_outputs: Tensor) -> Tensor:
# """Short summary.
# Args:
# hidden_state (Tensor): Description of parameter `h`.
# encoder_outputs (Tensor): Description of parameter `enc_out`.
# Shape:
# - x: :math:`(T, N, E)`.
# - output[0]: :math:`(T, N, 2 * E)`.
# - output[1]: :math:`(T, N, D)`.
# where T is the sequence length, N is the batch size, E is the
# embedding/encoder dimension, and D is the decoder dimension.
# Returns:
# Tensor: Description of returned object.
# """
# t, b = enc_out.shape[:2]
# # repeat decoder hidden state src_len times
# hidden_state = hidden_state.unsqueeze(1).repeat(1, t, 1)
# encoder_outputs = rearrange(encoder_outputs, "t b e2 -> b t e2")
# # Calculate the energy between the decoders previous hidden state and the
# # encoders hidden states.
# energy = torch.tanh(
# self.attn(torch.cat((hidden_state, encoder_outputs), dim=2))
# )
# attention = self.value(energy).squeeze(2)
# # Apply softmax on the attention to squeeze it between 0 and 1.
# attention = F.softmax(attention, dim=1)
# return attention
# class Decoder(nn.Module):
# def __init__(
# self,
# embedding_dim: int,
# encoder_dim: int,
# decoder_dim: int,
# output_dim: int,
# dropout_rate: float = 0.1,
# ) -> None:
# super().__init__()
# self.output_dim = output_dim
# self.embedding = nn.Embedding(output_dim, embedding_dim)
# self.attention = Attention(encoder_dim, decoder_dim)
# self.rnn = nn.GRU(
# input_size=2 * encoder_dim + embedding_dim, hidden_size=decoder_dim
# )
# self.head = nn.Linear(
# in_features=2 * encoder_dim + embedding_dim + decoder_dim,
# out_features=output_dim,
# )
# self.dropout = nn.Dropout(p=dropout_rate)
# def forward(
# self, trg: Tensor, hidden_state: Tensor, encoder_outputs: Tensor
# ) -> Tensor:
# # input = [batch size]
# # hidden = [batch size, dec hid dim]
# # encoder_outputs = [src len, batch size, enc hid dim * 2]
# trg = trg.unsqueeze(0)
# trg_embedded = self.dropout(self.embedding(trg))
# a = self.attention(hidden_state, encoder_outputs)
# weighted = torch.bmm(a, encoder_outputs)
# # Permutate the tensor.
# weighted = rearrange(weighted, "b a e2 -> a b e2")
# rnn_input = torch.cat((trg_embedded, weighted), dim=2)
# output, hidden = self.rnn(rnn_input, hidden.unsqueeze(0))
# # seq len, n layers and n directions will always be 1 in this decoder, therefore:
# # output = [1, batch size, dec hid dim]
# # hidden = [1, batch size, dec hid dim]
# # this also means that output == hidden
# assert (output == hidden).all()
# trg_embedded = trg_embedded.squeeze(0)
# output = output.squeeze(0)
# weighted = weighted.squeeze(0)
# logits = self.fc_out(torch.cat((output, weighted, trg_embedded), dim=1))
# # prediction = [batch size, output dim]
# return logits, hidden.squeeze(0)
# class NeuralMachineReader(nn.Module):
# def __init__(
# self,
# embedding_dim: int,
# encoder_dim: int,
# decoder_dim: int,
# output_dim: int,
# backbone: Optional[str] = None,
# backbone_args: Optional[Dict] = None,
# adaptive_pool_dim: Tuple = (None, 1),
# dropout_rate: float = 0.1,
# teacher_forcing_ratio: float = 0.5,
# ) -> None:
# super().__init__()
# self.backbone = configure_backbone(backbone, backbone_args)
# self.adaptive_pool = nn.AdaptiveAvgPool2d((adaptive_pool_dim))
# self.encoder = Encoder(embedding_dim, encoder_dim, decoder_dim, dropout_rate)
# self.decoder = Decoder(
# embedding_dim, encoder_dim, decoder_dim, output_dim, dropout_rate
# )
# self.teacher_forcing_ratio = teacher_forcing_ratio
# def extract_image_features(self, x: Tensor) -> Tensor:
# x = self.backbone(x)
# x = rearrange(x, "b c h w -> b w c h")
# x = self.adaptive_pool(x)
# x = x.squeeze(3)
# def forward(self, x: Tensor, trg: Tensor) -> Tensor:
# # x = [batch size, height, width]
# # trg = [trg len, batch size]
# z = self.extract_image_features(x)
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