# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""

import re
import string
import collections
from typing import Callable
import evaluate
import datasets


# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""

_DESCRIPTION = """\
Question-answering metrics (`Exact Match` and `F1`) for Musique-Answerable dataset. 

The implementation is taken from Musique repository.
https://github.com/StonyBrookNLP/musique
"""


_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predicted answers.
    references: list of ground truth answers. Each reference should be a list of 
        ground truth answers for the corresponding prediction.
Returns:
    exact_match: Exact match score,
    f1: F1 score over tokens
Examples:
    >>> my_new_module = evaluate.load("musique")
    >>> results = my_new_module.compute(
        references=[["New York City", "NYC"], ["Einstein", "Albert Einstein"]], 
        predictions=["New York City", "Albert Einstein"],
    )
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class musique(evaluate.Metric):
    """TODO: Question answering metrics (EM and F1) for Musique-Answerable dataset."""

    def _info(self):
        # TODO: Specifies the evaluate.EvaluationModuleInfo object
        return evaluate.MetricInfo(
            # This is the description that will appear on the modules page.
            module_type="metric",
            description=_DESCRIPTION,
            citation=_CITATION,
            inputs_description=_KWARGS_DESCRIPTION,
            # This defines the format of each prediction and reference
            features=datasets.Features(
                {
                    "predictions": datasets.Value("string"),
                    "references": datasets.features.Sequence(datasets.Value("string")),
                }
            ),
            # Homepage of the module for documentation
            homepage="https://huggingface.co/spaces/bdsaglam/musique",
            # Additional links to the codebase or references
            codebase_urls=["https://huggingface.co/spaces/bdsaglam/musique"],
            reference_urls=["https://github.com/StonyBrookNLP/musique"],
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        pass

    def _compute(self, predictions, references):
        """Returns the scores"""

        if len(predictions) != len(references):
            raise ValueError(
                "The number of predictions and references should be the same."
            )

        if len(predictions) == 0:
            return {"exact_match": 0.0, "f1": 0.0}

        exact_scores = [
            metric_max_over_ground_truths(compute_exact, prediction, reference)
            for prediction, reference in zip(predictions, references)
        ]
        f1_scores = [
            metric_max_over_ground_truths(compute_f1, prediction, reference)
            for prediction, reference in zip(predictions, references)
        ]
        return {
            "exact_match": sum(exact_scores) / len(exact_scores),
            "f1": sum(f1_scores) / len(f1_scores),
        }


# Source: https://github.com/StonyBrookNLP/musique/blob/main/metrics/answer.py


def normalize_answer(s):
    """Lower text and remove punctuation, articles and extra whitespace."""

    def remove_articles(text):
        regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
        return re.sub(regex, " ", text)

    def white_space_fix(text):
        return " ".join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


def get_tokens(s):
    if not s:
        return []
    return normalize_answer(s).split()


def compute_exact(a_gold, a_pred):
    return int(normalize_answer(a_gold) == normalize_answer(a_pred))


def compute_f1(a_gold, a_pred):
    gold_toks = get_tokens(a_gold)
    pred_toks = get_tokens(a_pred)
    common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
    num_same = sum(common.values())
    if len(gold_toks) == 0 or len(pred_toks) == 0:
        # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
        return int(gold_toks == pred_toks)
    if num_same == 0:
        return 0
    precision = 1.0 * num_same / len(pred_toks)
    recall = 1.0 * num_same / len(gold_toks)
    f1 = (2 * precision * recall) / (precision + recall)
    return f1


def metric_max_over_ground_truths(
    metric_fn: Callable[[str, str], float],
    prediction: str,
    ground_truths: list[str],
) -> float:
    scores_for_ground_truths = [
        metric_fn(prediction, ground_truth) for ground_truth in ground_truths
    ]
    return max(scores_for_ground_truths)