def __init__()

in pytext/models/seq_models/conv_encoder.py [0:0]

• 52 lines of code
• 9 McCabe index (conditional complexity)

    def __init__(self, dictionary, embed_tokens, layers, encoder_config):
        super().__init__()
        self.dropout = encoder_config.dropout

        input_embed_dim = embed_tokens.embedding_dim
        self.padding_idx = dictionary.get_pad_index()
        self.max_source_positions = encoder_config.max_source_positions

        self.embed_scale = math.sqrt(input_embed_dim)  # todo: try with input_embed_dim
        self.no_token_positional_embeddings = (
            encoder_config.no_token_positional_embeddings
        )
        # creating this is also conditional
        self.project_in_dim = (
            Linear(input_embed_dim, encoder_config.encoder_embed_dim)
            if encoder_config.encoder_embed_dim != input_embed_dim
            else PlaceholderIdentity()
        )
        self.embed_layer_norm = LayerNorm(encoder_config.encoder_embed_dim)

        self.combine_pos_embed = encoder_config.combine_pos_embed.value
        if encoder_config.combine_pos_embed == PostionalEmbedCombine.SUM:
            pos_embed_dim = encoder_config.encoder_embed_dim
        elif encoder_config.combine_pos_embed == PostionalEmbedCombine.CONCAT:
            pos_embed_dim = encoder_config.encoder_embed_dim - input_embed_dim
        else:
            raise NotImplementedError

        if not encoder_config.no_token_positional_embeddings:
            if encoder_config.positional_embedding_type == PostionalEmbedType.LEARNED:
                self.embed_positions = PositionalEmbedding(
                    encoder_config.max_source_positions,
                    pos_embed_dim,
                    self.padding_idx,
                )
            elif (
                encoder_config.positional_embedding_type
                == PostionalEmbedType.SINUSOIDAL
                or encoder_config.positional_embedding_type == PostionalEmbedType.HYBRID
            ):
                self.embed_positions = SinusoidalPositionalEmbedding(
                    pos_embed_dim,
                    self.padding_idx,
                    init_size=encoder_config.max_source_positions,
                    learned_embed=encoder_config.positional_embedding_type
                    == PostionalEmbedType.HYBRID,
                )
            else:
                raise NotImplementedError("Positional embedding type not supported")
        else:
            self.embed_positions = PlaceholderIdentity()

        self.layers = nn.ModuleList(layers)

        self.normalize = encoder_config.encoder_normalize_before
        if self.normalize:
            self.layer_norm = LayerNorm(encoder_config.encoder_embed_dim)
        else:
            self.layer_norm = PlaceholderIdentity()