# 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 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:
    kendall_tau_distance: Kendell's tau distance between predictions and references
    normalized_kendall_tau_distance: Kendell's tau distance between predictions and references normalized by the number of pairs
Examples:
    Examples should be written in doctest format, and should illustrate how
    to use the function.

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

# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class kendalltaudistance(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.Value('int64'),
                'references': datasets.Value('int64'),
            }),
            # 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 _compute(self, predictions, references):
        """Returns the scores"""
        # TODO: Compute the different scores of the module

        n = len(predictions)
        assert n == len(references), "The number of predictions and references should be the same"

        n_discordant_pairs = 0

        for i in range(len(predictions)):
            j = references.index(predictions[i])
            n_discordant_pairs += len(set(predictions[:i]).intersection(set(references[j:]))) + len(set(predictions[i+1:]).intersection(set(references[:j])))

        n_discordant_pairs = n_discordant_pairs / 2
        
        num_pairs = n * (n - 1) / 2
        
        return {
            'kendall_tau_distance': n_discordant_pairs,
            'normalized_kendall_tau_distance': n_discordant_pairs / num_pairs,
        }