# 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."""
from typing import List

import evaluate
import datasets

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

# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""

# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of predictions to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Returns:
    accuracy: description of the first score,
    another_score: description of the second score,
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

    >>> my_new_module = evaluate.load("my_new_module")
    >>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
    >>> print(results)
    {'accuracy': 1.0}
"""


def calculate_precision(
        predictions: List[List[str]],
        reference: List[List[str]]
) -> float:
    precision = 0
    count = 0

    for i, d in enumerate(reference):
        if len(d) == 0:
            continue

        predicted_titles = predictions[i]
        hits = 0
        for title in predicted_titles:
            if title in d:
                hits += 1

        if len(predicted_titles) != 0:
            precision += hits / len(predicted_titles)

        count += 1

    return precision / count


def calculate_recall(
        predictions: List[List[str]],
        reference: List[List[str]]
) -> float:
    recall = 0
    count = 0

    for i, d in enumerate(reference):
        if len(d) == 0:
            continue

        predicted_titles = predictions[i]
        hits = 0
        for title in predicted_titles:
            if title in d:
                hits += 1
        recall += hits / len(d)

        count += 1

    return recall / count


beta = 0.7


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class DocRetrieveMetrics(evaluate.Metric):
    """TODO: Short description of my evaluation module."""

    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.Sequence(datasets.Value("string")),
                "references": datasets.Sequence(datasets.Value("string")),
            }),
            # Homepage of the module for documentation
            homepage="http://module.homepage",
            # Additional links to the codebase or references
            codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
            reference_urls=["http://path.to.reference.url/new_module"]
        )

    def _download_and_prepare(self, dl_manager):
        """Optional: download external resources useful to compute the scores"""
        # TODO: Download external resources if needed
        pass

    def _compute(self, predictions, references):
        recall = calculate_recall(predictions, references)
        precision = calculate_precision(predictions, references)
        f_score = (1 + beta*beta) * precision * recall / (beta * beta*precision + recall)
        return {
            "f1": float(
                f_score
            )
        }