Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
        Download and cache the pre-trained model file if needed.

        Params:
            pretrained_model_name_or_path: either:
                - a str with the name of a pre-trained model to load selected in the list of:
                    . `transfo-xl`
                - a path or url to a pretrained model archive containing:
                    . `transfo_xl_config.json` a configuration file for the model
                    . `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
                - a path or url to a pretrained model archive containing:
                    . `bert_config.json` a configuration file for the model
                    . `model.chkpt` a TensorFlow checkpoint
            from_tf: should we load the weights from a locally saved TensorFlow checkpoint
            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
            *inputs, **kwargs: additional input for the specific Bert class
                (ex: num_labels for BertForSequenceClassification)

def from_pretrained(cls, pretrained_model_name_or_path, state_dict=None, cache_dir=None,
                        from_tf=False, *inputs, **kwargs):
        """
        Instantiate a TransfoXLPreTrainedModel from a pre-trained model file or a pytorch state dict.
        Download and cache the pre-trained model file if needed.

        Params:
            pretrained_model_name_or_path: either:
                - a str with the name of a pre-trained model to load selected in the list of:
                    . `transfo-xl`
                - a path or url to a pretrained model archive containing:
                    . `transfo_xl_config.json` a configuration file for the model
                    . `pytorch_model.bin` a PyTorch dump of a TransfoXLModel instance
                - a path or url to a pretrained model archive containing:
                    . `bert_config.json` a configuration file for the model
                    . `model.chkpt` a TensorFlow checkpoint
            from_tf: should we load the weights from a locally saved TensorFlow checkpoint
            cache_dir: an optional path to a folder in which the pre-trained models will be cached.
            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
            *inputs, **kwargs: additional input for the specific Bert class
                (ex: num_labels for BertForSequenceClassification)
        """
        if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
            archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
            config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
        else:
            archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
        # redirect to the cache, if necessary
        try:
            resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
        except EnvironmentError:
            logger.error(
                "Model name '{}' was not found in model name list ({}). "
                "We assumed '{}' was a path or url but couldn't find files {} and {} "
                "at this path or url.".format(
                    pretrained_model_name_or_path,
                    ', '.join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
                    pretrained_model_name_or_path,
                    archive_file, config_file))
            return None
        if resolved_archive_file == archive_file and resolved_config_file == config_file:
            logger.info("loading weights file {}".format(archive_file))
            logger.info("loading configuration file {}".format(config_file))
        else:
            logger.info("loading weights file {} from cache at {}".format(
                archive_file, resolved_archive_file))
            logger.info("loading configuration file {} from cache at {}".format(
                config_file, resolved_config_file))
        # Load config
        config = TransfoXLConfig.from_json_file(resolved_config_file)
        logger.info("Model config {}".format(config))
        # Instantiate model.
        model = cls(config, *inputs, **kwargs)
        if state_dict is None and not from_tf:
            state_dict = torch.load(resolved_archive_file, map_location='cpu')
        if from_tf:
            # Directly load from a TensorFlow checkpoint
            return load_tf_weights_in_transfo_xl(model, config, pretrained_model_name_or_path)

        missing_keys = []
        unexpected_keys = []
        error_msgs = []
        # copy state_dict so _load_from_state_dict can modify it
        metadata = getattr(state_dict, '_metadata', None)
        state_dict = state_dict.copy()
        if metadata is not None:
            state_dict._metadata = metadata

        def load(module, prefix=''):
            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
            module._load_from_state_dict(
                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
            for name, child in module._modules.items():
                if child is not None:
                    load(child, prefix + name + '.')

        start_prefix = ''
        if not hasattr(model, 'transformer') and any(s.startswith('transformer.') for s in state_dict.keys()):
            start_prefix = 'transformer.'
        load(model, prefix=start_prefix)

        if len(missing_keys) > 0:
            logger.info("Weights of {} not initialized from pretrained model: {}".format(
                model.__class__.__name__, missing_keys))
        if len(unexpected_keys) > 0:
            logger.info("Weights from pretrained model not used in {}: {}".format(
                model.__class__.__name__, unexpected_keys))
        if len(error_msgs) > 0:
            raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
                               model.__class__.__name__, "\n\t".join(error_msgs)))
        # Make sure we are still sharing the input and output embeddings
        if hasattr(model, 'tie_weights'):
            model.tie_weights()
        return model