(
        self,
        vocab_size_or_config_json_file=246534,
        n_positions=256,
        n_ctx=256,
        n_embd=1280,
        dff=8192,
        n_layer=48,
        n_head=16,
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-6,
        initializer_range=0.02,

        num_labels=1,
        summary_type='cls_index',
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        **kwargs
    )
Constructs CTRLConfig.

        Args:
            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `CTRLModel` or a configuration json file.
            n_positions: Number of positional embeddings.
            n_ctx: Size of the causal mask (usually same as n_positions).
            dff: Size of the inner dimension of the FFN.
            n_embd: Dimensionality of the embeddings and hidden states.
            n_layer: Number of hidden layers in the Transformer encoder.
            n_head: Number of attention heads for each attention layer in
                the Transformer encoder.
            layer_norm_epsilon: epsilon to use in the layer norm layers
            resid_pdrop: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attn_pdrop: The dropout ratio for the attention
                probabilities.
            embd_pdrop: The dropout ratio for the embeddings.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
Constructs CTRLConfig.
Constructs CTRLConfig .
def __init__(
        self,
        vocab_size_or_config_json_file=246534,
        n_positions=256,
        n_ctx=256,
        n_embd=1280,
        dff=8192,
        n_layer=48,
        n_head=16,
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-6,
        initializer_range=0.02,

        num_labels=1,
        summary_type='cls_index',
        summary_use_proj=True,
        summary_activation=None,
        summary_proj_to_labels=True,
        summary_first_dropout=0.1,
        **kwargs
    ):
        """Constructs CTRLConfig.

        Args:
            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `CTRLModel` or a configuration json file.
            n_positions: Number of positional embeddings.
            n_ctx: Size of the causal mask (usually same as n_positions).
            dff: Size of the inner dimension of the FFN.
            n_embd: Dimensionality of the embeddings and hidden states.
            n_layer: Number of hidden layers in the Transformer encoder.
            n_head: Number of attention heads for each attention layer in
                the Transformer encoder.
            layer_norm_epsilon: epsilon to use in the layer norm layers
            resid_pdrop: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attn_pdrop: The dropout ratio for the attention
                probabilities.
            embd_pdrop: The dropout ratio for the embeddings.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
        """
        super(CTRLConfig, self).__init__(**kwargs)

        self.vocab_size = vocab_size_or_config_json_file if isinstance(vocab_size_or_config_json_file, int) else -1
        self.n_ctx = n_ctx
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.dff = dff
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attn_pdrop = attn_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range

        self.num_labels = num_labels
        self.summary_type = summary_type
        self.summary_use_proj = summary_use_proj
        self.summary_activation = summary_activation
        self.summary_first_dropout = summary_first_dropout
        self.summary_proj_to_labels = summary_proj_to_labels
        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 not isinstance(vocab_size_or_config_json_file, int):
            raise ValueError(
                "First argument must be either a vocabulary size (int)"
                "or the path to a pretrained model config file (str)"
            )
def __init__ ( self , vocab_size_or_config_json_file = 246534 , n_positions = 256 , n_ctx = 256 , n_embd = 1280 , dff = 8192 , n_layer = 48 , n_head = 16 , resid_pdrop = 0.1 , embd_pdrop = 0.1 , attn_pdrop = 0.1 , layer_norm_epsilon = 1e-6 , initializer_range = 0.02 , num_labels = 1 , summary_type = 'cls_index' , summary_use_proj = True , summary_activation = None , summary_proj_to_labels = True , summary_first_dropout = 0.1 , * * kwargs ) : super ( CTRLConfig , self ) . __init__ ( * * kwargs ) self . vocab_size = vocab_size_or_config_json_file if isinstance ( vocab_size_or_config_json_file , int ) else - 1 self . n_ctx = n_ctx self . n_positions = n_positions self . n_embd = n_embd self . n_layer = n_layer self . n_head = n_head self . dff = dff self . resid_pdrop = resid_pdrop self . embd_pdrop = embd_pdrop self . attn_pdrop = attn_pdrop self . layer_norm_epsilon = layer_norm_epsilon self . initializer_range = initializer_range self . num_labels = num_labels self . summary_type = summary_type self . summary_use_proj = summary_use_proj self . summary_activation = summary_activation self . summary_first_dropout = summary_first_dropout self . summary_proj_to_labels = summary_proj_to_labels 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 not isinstance ( vocab_size_or_config_json_file , int ) : raise ValueError ( "First argument must be either a vocabulary size (int)" "or the path to a pretrained model config file (str)" )