def __init__()

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

• 71 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self,
                 vocab_size_or_config_json_file=267735,
                 cutoffs=[20000, 40000, 200000],
                 d_model=1024,
                 d_embed=1024,
                 n_head=16,
                 d_head=64,
                 d_inner=4096,
                 div_val=4,
                 pre_lnorm=False,
                 n_layer=18,
                 tgt_len=128,
                 ext_len=0,
                 mem_len=1600,
                 clamp_len=1000,
                 same_length=True,
                 proj_share_all_but_first=True,
                 attn_type=0,
                 sample_softmax=-1,
                 adaptive=True,
                 tie_weight=True,
                 dropout=0.1,
                 dropatt=0.0,
                 untie_r=True,
                 init="normal",
                 init_range=0.01,
                 proj_init_std=0.01,
                 init_std=0.02,
                 **kwargs):
        """Constructs TransfoXLConfig.
        """
        super(TransfoXLConfig, 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_token = vocab_size_or_config_json_file
            self.cutoffs = []
            self.cutoffs.extend(cutoffs)
            self.tie_weight = tie_weight
            if proj_share_all_but_first:
                self.tie_projs = [False] + [True] * len(self.cutoffs)
            else:
                self.tie_projs = [False] + [False] * len(self.cutoffs)
            self.d_model = d_model
            self.d_embed = d_embed
            self.d_head = d_head
            self.d_inner = d_inner
            self.div_val = div_val
            self.pre_lnorm = pre_lnorm
            self.n_layer = n_layer
            self.n_head = n_head
            self.tgt_len = tgt_len
            self.ext_len = ext_len
            self.mem_len = mem_len
            self.same_length = same_length
            self.attn_type = attn_type
            self.clamp_len = clamp_len
            self.sample_softmax = sample_softmax
            self.adaptive = adaptive
            self.dropout = dropout
            self.dropatt = dropatt
            self.untie_r = untie_r
            self.init = init
            self.init_range = init_range
            self.proj_init_std = proj_init_std
            self.init_std = init_std
        else:
            raise ValueError("First argument must be either a vocabulary size (int)"
                             " or the path to a pretrained model config file (str)")