(
        self,
        vocab_size_or_config_json_file=40478,
        n_positions=512,
        n_ctx=512,
        n_embd=768,
        n_layer=12,
        n_head=12,
        afn="gelu",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        predict_special_tokens=True,

        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 OpenAIGPTConfig.
Constructs OpenAIGPTConfig.
Constructs OpenAIGPTConfig .
def __init__(
        self,
        vocab_size_or_config_json_file=40478,
        n_positions=512,
        n_ctx=512,
        n_embd=768,
        n_layer=12,
        n_head=12,
        afn="gelu",
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        attn_pdrop=0.1,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        predict_special_tokens=True,

        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 OpenAIGPTConfig.
        """
        super(OpenAIGPTConfig, 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.vocab_size = vocab_size_or_config_json_file
            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.afn = afn
            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.predict_special_tokens = predict_special_tokens

            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
        else:
            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 = 40478 , n_positions = 512 , n_ctx = 512 , n_embd = 768 , n_layer = 12 , n_head = 12 , afn = "gelu" , resid_pdrop = 0.1 , embd_pdrop = 0.1 , attn_pdrop = 0.1 , layer_norm_epsilon = 1e-5 , initializer_range = 0.02 , predict_special_tokens = True , 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 ( OpenAIGPTConfig , 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 . vocab_size = vocab_size_or_config_json_file 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 . afn = afn 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 . predict_special_tokens = predict_special_tokens 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 else : raise ValueError ( "First argument must be either a vocabulary size (int)" "or the path to a pretrained model config file (str)" )