def __init__()

in pytorch-transformers/pytorch_transformers/modeling_xlm.py [0:0]

• 75 lines of code
• 6 McCabe index (conditional complexity)

    def __init__(self,
                 vocab_size_or_config_json_file=30145,
                 emb_dim=2048,
                 n_layers=12,
                 n_heads=16,
                 dropout=0.1,
                 attention_dropout=0.1,
                 gelu_activation=True,
                 sinusoidal_embeddings=False,
                 causal=False,
                 asm=False,
                 n_langs=1,
                 use_lang_emb=True,
                 max_position_embeddings=512,
                 embed_init_std=2048 ** -0.5,
                 layer_norm_eps=1e-12,
                 init_std=0.02,
                 bos_index=0,
                 eos_index=1,
                 pad_index=2,
                 unk_index=3,
                 mask_index=5,
                 is_encoder=True,

                 finetuning_task=None,
                 num_labels=2,
                 summary_type='first',
                 summary_use_proj=True,
                 summary_activation=None,
                 summary_proj_to_labels=True,
                 summary_first_dropout=0.1,
                 start_n_top=5,
                 end_n_top=5,
                 **kwargs):
        """Constructs XLMConfig.
        """
        super(XLMConfig, self).__init__(**kwargs)

        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
                        and isinstance(vocab_size_or_config_json_file, unicode)):
            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
                json_config = json.loads(reader.read())
            for key, value in json_config.items():
                self.__dict__[key] = value
        elif isinstance(vocab_size_or_config_json_file, int):
            self.n_words = vocab_size_or_config_json_file
            self.emb_dim = emb_dim
            self.n_layers = n_layers
            self.n_heads = n_heads
            self.dropout = dropout
            self.attention_dropout = attention_dropout
            self.gelu_activation = gelu_activation
            self.sinusoidal_embeddings = sinusoidal_embeddings
            self.causal = causal
            self.asm = asm
            self.n_langs = n_langs
            self.use_lang_emb = use_lang_emb
            self.layer_norm_eps = layer_norm_eps
            self.bos_index = bos_index
            self.eos_index = eos_index
            self.pad_index = pad_index
            self.unk_index = unk_index
            self.mask_index = mask_index
            self.is_encoder = is_encoder
            self.max_position_embeddings = max_position_embeddings
            self.embed_init_std = embed_init_std
            self.init_std = init_std
            self.finetuning_task = finetuning_task
            self.num_labels = num_labels
            self.summary_type = summary_type
            self.summary_use_proj = summary_use_proj
            self.summary_activation = summary_activation
            self.summary_proj_to_labels = summary_proj_to_labels
            self.summary_first_dropout = summary_first_dropout
            self.start_n_top = start_n_top
            self.end_n_top = end_n_top
        else:
            raise ValueError("First argument must be either a vocabulary size (int)"
                             " or the path to a pretrained model config file (str)")