# This code is adapted from
# https://pytorch.org/tutorials/beginner/transformer_tutorial.html

"""
Language Modeling with nn.Transformer and TorchText
===============================================================
This is a tutorial on training a sequence-to-sequence model that uses the
`nn.Transformer <https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html>`__ module.
The PyTorch 1.2 release includes a standard transformer module based on the
paper `Attention is All You Need <https://arxiv.org/pdf/1706.03762.pdf>`__.
Compared to Recurrent Neural Networks (RNNs), the transformer model has proven
to be superior in quality for many sequence-to-sequence tasks while being more
parallelizable. The ``nn.Transformer`` module relies entirely on an attention
mechanism (implemented as
`nn.MultiheadAttention <https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html>`__)
to draw global dependencies between input and output. The ``nn.Transformer``
module is highly modularized such that a single component (e.g.,
`nn.TransformerEncoder <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html>`__)
can be easily adapted/composed.
.. image:: ../_static/img/transformer_architecture.jpg
"""
######################################################################
# Define the model
# ----------------
#


######################################################################
# In this tutorial, we train a ``nn.TransformerEncoder`` model on a
# language modeling task. The language modeling task is to assign a
# probability for the likelihood of a given word (or a sequence of words)
# to follow a sequence of words. A sequence of tokens are passed to the embedding
# layer first, followed by a positional encoding layer to account for the order
# of the word (see the next paragraph for more details). The
# ``nn.TransformerEncoder`` consists of multiple layers of
# `nn.TransformerEncoderLayer <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html>`__.
# Along with the input sequence, a square attention mask is required because the
# self-attention layers in ``nn.TransformerEncoder`` are only allowed to attend
# the earlier positions in the sequence. For the language modeling task, any
# tokens on the future positions should be masked. To produce a probability
# distribution over output words, the output of the ``nn.TransformerEncoder``
# model is passed through a linear layer followed by a log-softmax function.
#

import math

import torch
from torch import Tensor, nn
from torch.nn import Embedding, TransformerEncoder, TransformerEncoderLayer


class TransformerModel(nn.Module):
    def __init__(
        self,
        ntoken: int,
        d_model: int,
        nhead: int,
        d_hid: int,
        nlayers: int,
        dropout: float = 0.5,
        activation: str = "gelu",
        max_len: int = 10,
    ):
        super().__init__()
        self.model_type = 'Transformer'
        self.encoder = Embedding(ntoken, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout, max_len)
        encoder_layers = TransformerEncoderLayer(
            d_model,
            nhead,
            d_hid,
            dropout,
            batch_first=True,
            activation=activation,
        )
        self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
        self.d_model = d_model
        self.decoder = nn.Linear(d_model, ntoken)

        self.init_weights()

    def init_weights(self) -> None:
        initrange = 0.1
        self.encoder.weight.data.uniform_(-initrange, initrange)
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:
        """
        Args:
            src: Tensor, shape [seq_len, batch_size]
            src_mask: Tensor, shape [seq_len, seq_len]
        Returns:
            output Tensor of shape [seq_len, batch_size, ntoken]
        """
        src = self.encoder(src) * math.sqrt(self.d_model)
        src = self.pos_encoder(src)
        output = self.transformer_encoder(src, src_mask)
        output = self.decoder(output)
        return output


def generate_square_subsequent_mask(sz: int, device=None) -> Tensor:
    """Generates an upper-triangular matrix of -inf, with zeros on diag."""
    return (
        torch.triu(
            torch.ones((sz, sz), device=device, dtype=torch.float16), diagonal=1
        )
        * torch.finfo(torch.float16).min
    )


######################################################################
# ``PositionalEncoding`` module injects some information about the
# relative or absolute position of the tokens in the sequence. The
# positional encodings have the same dimension as the embeddings so that
# the two can be summed. Here, we use ``sine`` and ``cosine`` functions of
# different frequencies.
#


class PositionalEncoding(nn.Module):
    def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)

        position = torch.arange(max_len).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
        )
        pe = torch.zeros(max_len, 1, d_model)
        pe[:, 0, 0::2] = torch.sin(position * div_term)
        pe[:, 0, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe.transpose(1, 0))

    def forward(self, x: Tensor) -> Tensor:
        """
        Args:
            x: Tensor, shape [seq_len, batch_size, embedding_dim]
        """
        x = x + self.pe[:, : x.size(1), :]
        return self.dropout(x)
