src/transformers/models/fsmt/configuration_fsmt.py/FSMTConfig/__init__
class FSMTConfig:    def __init__(
        self,
        langs=["en", "de"],
        src_vocab_size=42024,
        tgt_vocab_size=42024,
        activation_function="relu",
        d_model=1024,
        max_length=200,
        max_position_embeddings=1024,
        encoder_ffn_dim=4096,
        encoder_layers=12,
        encoder_attention_heads=16,
        encoder_layerdrop=0.0,
        decoder_ffn_dim=4096,
        decoder_layers=12,
        decoder_attention_heads=16,
        decoder_layerdrop=0.0,
        attention_dropout=0.0,
        dropout=0.1,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        is_encoder_decoder=True,
        scale_embedding=True,
        tie_word_embeddings=False,
        num_beams=5,
        length_penalty=1.0,
        early_stopping=False,
        use_cache=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        forced_eos_token_id=2,
        **common_kwargs
    ):
        self.langs = langs
        self.src_vocab_size = src_vocab_size
        self.tgt_vocab_size = tgt_vocab_size
        self.d_model = d_model  # encoder_embed_dim and decoder_embed_dim
        self.max_length = max_length

        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = self.num_hidden_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.init_std = init_std  # Normal(0, this parameter)
        self.activation_function = activation_function

        self.num_beams = num_beams
        self.length_penalty = length_penalty
        self.early_stopping = early_stopping

        self.decoder = DecoderConfig(vocab_size=tgt_vocab_size, bos_token_id=eos_token_id)
        if "decoder" in common_kwargs:
            del common_kwargs["decoder"]

        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.dropout = dropout

        self.use_cache = use_cache
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            decoder_start_token_id=decoder_start_token_id,
            is_encoder_decoder=is_encoder_decoder,
            tie_word_embeddings=tie_word_embeddings,
            forced_eos_token_id=forced_eos_token_id,
            **common_kwargs,
        )