utils.py

import yaml
from tqdm.auto import tqdm
import collections
import numpy as np
from nltk.translate.bleu_score import sentence_bleu

def load_yaml_file(path_yaml: str):
    with open(path_yaml, 'r') as stream:
        try:
            config = yaml.safe_load(stream)
        except yaml.YAMLError as exc:
            print(exc)
    return config


def postprocess_qa_predictions(examples, features, raw_predictions, tokenizer, squad_v2, n_best_size, max_answer_length):
    all_start_logits, all_end_logits = raw_predictions
    # Build a map example to its corresponding features.
    example_id_to_index = {k: i for i, k in enumerate(examples["id"])}
    features_per_example = collections.defaultdict(list)
    for i, feature in enumerate(features):
        features_per_example[example_id_to_index[feature["example_id"]]].append(i)

    # The dictionaries we have to fill.
    predictions = collections.OrderedDict()

    # Logging.
    print(f"Post-processing {len(examples)} example predictions split into {len(features)} features.")

    # Let's loop over all the examples!
    for example_index, example in enumerate(tqdm(examples)):
        # Those are the indices of the features associated to the current example.
        feature_indices = features_per_example[example_index]

        min_null_score = None # Only used if squad_v2 is True.
        valid_answers = []

        context = example["context"]
        # Looping through all the features associated to the current example.
        for feature_index in feature_indices:
            # We grab the predictions of the model for this feature.
            start_logits = all_start_logits[feature_index]
            end_logits = all_end_logits[feature_index]
            # This is what will allow us to map some the positions in our logits to span of texts in the original
            # context.
            offset_mapping = features[feature_index]["offset_mapping"]

            # Update minimum null prediction.
            cls_index = features[feature_index]["input_ids"].index(tokenizer.cls_token_id)
            feature_null_score = start_logits[cls_index] + end_logits[cls_index]
            if min_null_score is None or min_null_score < feature_null_score:
                min_null_score = feature_null_score

            # Go through all possibilities for the `n_best_size` greater start and end logits.
            start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()
            end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
            for start_index in start_indexes:
                for end_index in end_indexes:
                    # Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
                    # to part of the input_ids that are not in the context.
                    try:
                        if (
                                start_index >= len(offset_mapping)
                                or end_index >= len(offset_mapping)
                                or offset_mapping[start_index] is None
                                or offset_mapping[end_index] is None
                                or len(offset_mapping[start_index]) == 0
                                or len(offset_mapping[end_index]) == 0
                        ):
                            continue
                        # Don't consider answers with a length that is either < 0 or > max_answer_length.
                        if end_index <= start_index or end_index - start_index + 1 > max_answer_length:
                            continue
                        start_char = offset_mapping[start_index][0]
                        end_char = offset_mapping[end_index][1]
                        valid_answers.append(
                            {
                                "score": start_logits[start_index] + end_logits[end_index],
                                "text": context[start_char: end_char]
                            }
                        )
                    except:
                        global offset_mapping_try
                        global start_index_try
                        global end_index_try
                        start_index_try = start_index
                        end_index_try = end_index
                        offset_mapping_try = offset_mapping
                        raise SystemExit(0)

        if len(valid_answers) > 0:
            best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0]
        else:
            # In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
            # failure.
            best_answer = {"text": "", "score": 0.0}

        # Let's pick our final answer: the best one or the null answer (only for squad_v2)
        if not squad_v2:
            predictions[example["id"]] = best_answer["text"]
        else:
            answer = best_answer["text"] if best_answer["score"] > min_null_score else ""
            predictions[example["id"]] = answer

    return predictions


def prepare_validation_features(examples, tokenizer, PAD_RIGHT, MAX_LENGTH, DOC_STRIDE):
    # Some of the questions have lots of whitespace on the left, which is not useful and will make the
    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
    # left whitespace
    examples["question"] = [q.lstrip() for q in examples["question"]]

    # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    tokenized_examples = tokenizer(
        examples["question" if PAD_RIGHT else "context"],
        examples["context" if PAD_RIGHT else "question"],
        truncation="only_second" if PAD_RIGHT else "only_first",
        max_length=MAX_LENGTH,
        stride=DOC_STRIDE,
        return_overflowing_tokens=True,
        return_offsets_mapping=True,
        padding="max_length",
    )

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")

    # We keep the example_id that gave us this feature and we will store the offset mappings.
    tokenized_examples["example_id"] = []

    for i in range(len(tokenized_examples["input_ids"])):
        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        sequence_ids = tokenized_examples.sequence_ids(i)
        context_index = 1 if PAD_RIGHT else 0

        # One example can give several spans, this is the index of the example containing this span of text.
        sample_index = sample_mapping[i]
        tokenized_examples["example_id"].append(examples["id"][sample_index])

        # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
        # position is part of the context or not.
        tokenized_examples["offset_mapping"][i] = [
            (o if sequence_ids[k] == context_index else None)
            for k, o in enumerate(tokenized_examples["offset_mapping"][i])
        ]

    return tokenized_examples


def prepare_train_features(examples, tokenizer, PAD_RIGHT, MAX_LENGTH, DOC_STRIDE):
    # Some of the questions have lots of whitespace on the left, which is not useful and will make the
    # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
    # left whitespace
    examples["question"] = [q.lstrip() for q in examples["question"]]

    # Tokenize our examples with truncation and padding, but keep the overflows using a stride. This results
    # in one example possible giving several features when a context is long, each of those features having a
    # context that overlaps a bit the context of the previous feature.
    tokenized_examples = tokenizer(
        examples["question" if PAD_RIGHT else "context"],
        examples["context" if PAD_RIGHT else "question"],
        truncation="only_second" if PAD_RIGHT else "only_first",
        max_length=MAX_LENGTH,
        stride=DOC_STRIDE,
        return_overflowing_tokens=True,
        return_offsets_mapping=True,
        padding="max_length",
    )

    # Since one example might give us several features if it has a long context, we need a map from a feature to
    # its corresponding example. This key gives us just that.
    sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
    # The offset mappings will give us a map from token to character position in the original context. This will
    # help us compute the start_positions and end_positions.
    offset_mapping = tokenized_examples.pop("offset_mapping")

    # Let's label those examples!
    tokenized_examples["start_positions"] = []
    tokenized_examples["end_positions"] = []

    for i, offsets in enumerate(offset_mapping):
        # We will label impossible answers with the index of the CLS token.
        input_ids = tokenized_examples["input_ids"][i]
        cls_index = input_ids.index(tokenizer.cls_token_id)

        # Grab the sequence corresponding to that example (to know what is the context and what is the question).
        sequence_ids = tokenized_examples.sequence_ids(i)

        # One example can give several spans, this is the index of the example containing this span of text.
        sample_index = sample_mapping[i]
        answers = examples["answers"][sample_index]
        # If no answers are given, set the cls_index as answer.
        if len(answers["answer_start"]) == 0:
            tokenized_examples["start_positions"].append(cls_index)
            tokenized_examples["end_positions"].append(cls_index)
        else:
            # Start/end character index of the answer in the text.
            start_char = answers["answer_start"][0]
            end_char = start_char + len(answers["text"][0])

            # Start token index of the current span in the text.
            token_start_index = 0
            while sequence_ids[token_start_index] != (1 if PAD_RIGHT else 0):
                token_start_index += 1

            # End token index of the current span in the text.
            token_end_index = len(input_ids) - 1
            while sequence_ids[token_end_index] != (1 if PAD_RIGHT else 0):
                token_end_index -= 1

            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
            if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
            else:
                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
                # Note: we could go after the last offset if the answer is the last word (edge case).
                while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
                    token_start_index += 1
                tokenized_examples["start_positions"].append(token_start_index - 1)
                while offsets[token_end_index][1] >= end_char:
                    token_end_index -= 1
                tokenized_examples["end_positions"].append(token_end_index + 1)

    return tokenized_examples

def calculate_bleu_score(formatted_predictions, references):
    bleus = []
    n_bleu_scores = 0
    for i in range(len(formatted_predictions)):
        fp = formatted_predictions[i]['prediction_text']
        ref = references[i]['answers']['text']
        if (len(fp) == 0) or (len(ref) == 0):
            continue
        #print('fp: ', fp, '\nref: ', ref)
        bleu = sentence_bleu(ref, fp, weights=(.5,.5))
        bleus.append(bleu)
        n_bleu_scores += 1
    return bleus

def calculate_bleu_score_new(formatted_predictions, references):
    bleus = []
    n_bleu_scores = 0
    for i in range(len(formatted_predictions)):
        fp = formatted_predictions[i]['prediction_text']
        ref = references[i]['answers']['text']
        if (len(fp) == 0) and (len(ref) == 0):
            bleus.append(1)
            continue
        if (len(fp)==0) and (len(ref) > 0):
            bleus.append(0)
            continue
        if (len(fp) > 0) and (len(ref)==0):
            bleus.append(0)
            continue
        bleu = sentence_bleu(ref, fp, weights=(.5,.5))
        bleus.append(bleu)
        n_bleu_scores += 1
    return bleus