""" ContextGate module """
import torch
import torch.nn as nn


def context_gate_factory(
    gate_type, embeddings_size, decoder_size, attention_size, output_size
):
    """Returns the correct ContextGate class"""

    gate_types = {
        "source": SourceContextGate,
        "target": TargetContextGate,
        "both": BothContextGate,
    }

    assert gate_type in gate_types, "Not valid ContextGate type: {0}".format(gate_type)
    return gate_types[gate_type](
        embeddings_size, decoder_size, attention_size, output_size
    )


class ContextGate(nn.Module):
    """
    Context gate is a decoder module that takes as input the previous word
    embedding, the current decoder state and the attention state, and
    produces a gate.
    The gate can be used to select the input from the target side context
    (decoder state), from the source context (attention state) or both.
    """

    def __init__(self, embeddings_size, decoder_size, attention_size, output_size):
        super(ContextGate, self).__init__()
        input_size = embeddings_size + decoder_size + attention_size
        self.gate = nn.Linear(input_size, output_size, bias=True)
        self.sig = nn.Sigmoid()
        self.source_proj = nn.Linear(attention_size, output_size)
        self.target_proj = nn.Linear(embeddings_size + decoder_size, output_size)

    def forward(self, prev_emb, dec_state, attn_state):
        input_tensor = torch.cat((prev_emb, dec_state, attn_state), dim=1)
        z = self.sig(self.gate(input_tensor))
        proj_source = self.source_proj(attn_state)
        proj_target = self.target_proj(torch.cat((prev_emb, dec_state), dim=1))
        return z, proj_source, proj_target


class SourceContextGate(nn.Module):
    """Apply the context gate only to the source context"""

    def __init__(self, embeddings_size, decoder_size, attention_size, output_size):
        super(SourceContextGate, self).__init__()
        self.context_gate = ContextGate(
            embeddings_size, decoder_size, attention_size, output_size
        )
        self.tanh = nn.Tanh()

    def forward(self, prev_emb, dec_state, attn_state):
        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)
        return self.tanh(target + z * source)


class TargetContextGate(nn.Module):
    """Apply the context gate only to the target context"""

    def __init__(self, embeddings_size, decoder_size, attention_size, output_size):
        super(TargetContextGate, self).__init__()
        self.context_gate = ContextGate(
            embeddings_size, decoder_size, attention_size, output_size
        )
        self.tanh = nn.Tanh()

    def forward(self, prev_emb, dec_state, attn_state):
        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)
        return self.tanh(z * target + source)


class BothContextGate(nn.Module):
    """Apply the context gate to both contexts"""

    def __init__(self, embeddings_size, decoder_size, attention_size, output_size):
        super(BothContextGate, self).__init__()
        self.context_gate = ContextGate(
            embeddings_size, decoder_size, attention_size, output_size
        )
        self.tanh = nn.Tanh()

    def forward(self, prev_emb, dec_state, attn_state):
        z, source, target = self.context_gate(prev_emb, dec_state, attn_state)
        return self.tanh((1.0 - z) * target + z * source)
