added this function for evaluation

            raw_text = None

            context_count += 1

def generate_samples_eval(model, context, max_gen_length, eos_token_id, do_sample):

    # Generate samples for lm evaluation

    # NEED TO THINK ABOUT eos token

    args = get_args()

    tokenizer = get_tokenizer()

    raw_text_len = len(context)

    model.eval()

    context_tokens = tokenizer.tokenize(context)

    args.out_seq_length = max_gen_length + len(context_tokens)

    args.recompute = True #set this default value

    args.eos_id = eos_token_id

    if not do_sample:

        args.greedy = True

    else:

        # set similar to huggngface

        args.top_p = 1.0

        args.temperature = 1.0

        args.top_k = 50

    with torch.no_grad():

        token_stream = get_token_stream(model, [context_tokens])

        for counter, decode_tokens in enumerate(token_stream):

            decode_tokens, _ = decode_tokens

            decode_tokens = decode_tokens[0].cpu().numpy().tolist()

            trim_decode_tokens = tokenizer.detokenize(

                decode_tokens)[raw_text_len:]

            if counter == args.out_seq_length:

                break

    return trim_decode_tokens

def generate_samples_interactive(model, print_frequency=24):

    model.eval()

    with torch.no_grad():

        context_length = context_lengths.min().item()

        if hasattr(args, 'eos_id'):

            eos_id = args.eos_id

        else:

            eos_id = tokenizer.eod

        counter = 0