utils.py

import numpy as np
import pandas as pd
from tqdm.auto import tqdm
import collections
import os
import json
import warnings
import random
import torch
import datetime
from pathlib import Path
warnings.simplefilter('ignore')
from torch.utils.data import Dataset, DataLoader, BatchSampler, Sampler

class ChaiiRandomSampler(Sampler):
    def __init__(self, data_source, downsample=1.0):
        self.data_source = data_source
        self.downsample = downsample
        self.chaii_indices = []
        self.ext_indices = []
        for i, item in enumerate(self.data_source):
            if item['src'] == 0:
                self.chaii_indices.append(i)
            else:
                self.ext_indices.append(i)
        self.chaii_indices = torch.tensor(self.chaii_indices, dtype=torch.long)
        self.ext_indices = torch.tensor(self.ext_indices, dtype=torch.long)
        self.n_chaii = len(self.chaii_indices)
        self.n_ext = int(len(self.ext_indices)*self.downsample)

    def __iter__(self):
        sampled_ext_indices_indices = torch.randperm(len(self.ext_indices))[:self.n_ext].long()
#         print(type(sampled_ext_indices_indices))
        sampled_ext_indices = self.ext_indices[sampled_ext_indices_indices]
        indices = torch.cat([self.chaii_indices, sampled_ext_indices])
        indices_indices = torch.randperm(self.n_chaii+self.n_ext).long()
        return iter(indices[indices_indices].tolist())

    def __len__(self):
        return len(self.data_source)

def get_time():
    timenow = str(datetime.datetime.now()).split('.')[0]
    timenow = '-'.join(timenow.split())
    return timenow


def seed_everything(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True

def log_hyp(out_dir, hyp):
    with open(os.path.join(out_dir, 'hyp.txt'), 'w') as f:
        json.dump(hyp, f, indent=4)

def log_scores(out_dir, scores):
    with open(os.path.join(out_dir, 'scores.txt'), 'w') as f:
        for i, s in enumerate(scores):
            f.write(f"{i}, {s}\n")
        f.write(str(np.mean(scores)))

def jaccard(row): 
    str1 = row[0]
    str2 = row[1]
    a = set(str1.lower().split()) 
    b = set(str2.lower().split())
    c = a.intersection(b)
    return float(len(c)) / (len(a) + len(b) - len(c))

def convert_answers(r):
    start = r[0]
    text = r[1]
    return {
        'answer_start': [start],
        'text': [text]
    }

def prepare_train_features(examples, tokenizer, max_length=384, doc_stride=128, pad_on_right=True):
    # 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_on_right else "context"],
        examples["context" if pad_on_right else "question"],
        truncation="only_second" if pad_on_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_on_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_on_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 prepare_validation_features(examples, tokenizer, max_length=384, doc_stride=128, pad_on_right=True):
    # 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_on_right else "context"],
        examples["context" if pad_on_right else "question"],
        truncation="only_second" if pad_on_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_on_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 postprocess_qa_predictions(examples, features, raw_predictions, tokenizer, n_best_size=20, max_answer_length=30):
    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 qa predictions: {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.
                    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
                    ):
                        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]
                        }
                    )
        
        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)
        predictions[example["id"]] = best_answer["text"]

    return predictions

# TODO
def get_token_logits():
    raise NotImplementedError

def read_squad(path):
    path = Path(path)
    with open(path, 'rb') as f:
        squad_dict = json.load(f)

    contexts = []
    questions = []
    answers = []
    imp = 0
    p = 0
    for group in squad_dict['data']:
        for passage in group['paragraphs']:
            context = passage['context']
            for qa in passage['qas']:
                is_impossible = qa['is_impossible']
                question = qa['question']
                contexts.append(context)
                questions.append(question)
                if is_impossible:
                    imp += 1        
                    answers.append({'text': [], 'answer_start': []})
                else:
                    p += 1
                    text = qa['answers'][0]['text']
                    answer_start = qa['answers'][0]['answer_start']
                    answers.append({'text': [text], 'answer_start': [answer_start]})
    print(f'imp {imp}, p {p}')
    return {'id': [i for i in range(len(contexts))], 'context': contexts, 'question': questions, 'answers': answers}

def read_squad_enta(path):
    path = Path(path)
    with open(path, 'r') as f:
        squad_dict = json.load(f)
    return squad_dict

def prepare_train_features_v2(examples, tokenizer, max_length=384, doc_stride=128, pad_on_right=True):
    # 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_on_right else "context"],
        examples["context" if pad_on_right else "question"],
        truncation="only_second" if pad_on_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"] = []
    
    # We keep the example_id that gave us this feature and we will store the offset mappings.
    tokenized_examples["src"] = []

    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]
        
        # src
        if examples["src"][sample_index] == 'chaii':
            tokenized_examples["src"].append(0)
        elif examples["src"][sample_index] == 'xquad' or examples["src"][sample_index] == 'mlqa':
            tokenized_examples["src"].append(1)
        else:
            raise NameError
        
        # 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_on_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_on_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