Delete summ.py

summ.py

@@ -1,133 +0,0 @@
-import logging
-
-import torch
-from tqdm.auto import tqdm
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-
-def load_model_and_tokenizer(model_name):
-    """
-    load_model_and_tokenizer - a function that loads a model and tokenizer from huggingface
-    Args:
-        model_name (str): the name of the model to load
-    Returns:
-        AutoModelForSeq2SeqLM: the model
-        AutoTokenizer: the tokenizer
-    """
-
-    model = AutoModelForSeq2SeqLM.from_pretrained(
-        model_name,
-        # low_cpu_mem_usage=True,
-        # use_cache=False,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = model.to("cuda") if torch.cuda.is_available() else model
-
-    logging.info(f"Loaded model {model_name}")
-    return model, tokenizer
-
-
-def summarize_and_score(ids, mask, model, tokenizer, **kwargs):
-    """
-    summarize_and_score - given a batch of ids and a mask, return a summary and a score for the summary
-    Args:
-        ids (): the batch of ids
-        mask (): the attention mask for the batch
-        model   (): the model to use for summarization
-        tokenizer (): the tokenizer to use for summarization
-    Returns:
-        str: the summary of the batch
-    """
-
-    ids = ids[None, :]
-    mask = mask[None, :]
-
-    input_ids = ids.to("cuda") if torch.cuda.is_available() else ids
-    attention_mask = mask.to("cuda") if torch.cuda.is_available() else mask
-
-    global_attention_mask = torch.zeros_like(attention_mask)
-    # put global attention on <s> token
-    global_attention_mask[:, 0] = 1
-
-    summary_pred_ids = model.generate(
-        input_ids,
-        attention_mask=attention_mask,
-        global_attention_mask=global_attention_mask,
-        output_scores=True,
-        return_dict_in_generate=True,
-        **kwargs,
-    )
-    summary = tokenizer.batch_decode(
-        summary_pred_ids.sequences,
-        skip_special_tokens=True,
-        remove_invalid_values=True,
-    )
-    score = round(summary_pred_ids.sequences_scores.cpu().numpy()[0], 4)
-
-    return summary, score
-
-
-def summarize_via_tokenbatches(
-    input_text: str,
-    model,
-    tokenizer,
-    batch_length=2048,
-    batch_stride=16,
-    **kwargs,
-):
-    """
-    summarize_via_tokenbatches - a function that takes a string and returns a summary
-    Args:
-        input_text (str): the text to summarize
-        model (): the model to use for summarization
-        tokenizer (): the tokenizer to use for summarization
-        batch_length (int, optional): the length of each batch. Defaults to 2048.
-        batch_stride (int, optional): the stride of each batch. Defaults to 16. The stride is the number of tokens that overlap between batches.
-    Returns:
-        str: the summary
-    """
-    # log all input parameters
-    if batch_length < 512:
-        batch_length = 512
-        print("WARNING: batch_length was set to 512")
-    print(
-        f"input parameters: {kwargs}, batch_length={batch_length}, batch_stride={batch_stride}"
-    )
-    encoded_input = tokenizer(
-        input_text,
-        padding="max_length",
-        truncation=True,
-        max_length=batch_length,
-        stride=batch_stride,
-        return_overflowing_tokens=True,
-        add_special_tokens=False,
-        return_tensors="pt",
-    )
-
-    in_id_arr, att_arr = encoded_input.input_ids, encoded_input.attention_mask
-    gen_summaries = []
-
-    pbar = tqdm(total=len(in_id_arr))
-
-    for _id, _mask in zip(in_id_arr, att_arr):
-
-        result, score = summarize_and_score(
-            ids=_id,
-            mask=_mask,
-            model=model,
-            tokenizer=tokenizer,
-            **kwargs,
-        )
-        score = round(float(score), 4)
-        _sum = {
-            "input_tokens": _id,
-            "summary": result,
-            "summary_score": score,
-        }
-        gen_summaries.append(_sum)
-        print(f"\t{result[0]}\nScore:\t{score}")
-        pbar.update()
-
-    pbar.close()
-
-    return gen_summaries