# 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
from itertools import repeat


# 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 prediction
        should be an input id.
    references: list of reference for each prediction. Each
        reference should be an input id.
    actions_seen: number of actions token seen before generating the predicted action token.
    max_actions_seen: the number of scores to calculate. For example, with max_actions_seen = 5,
        it will calculate score for prediction with actions_seen = 0, 1, 2, 3, 4, 5.
Returns:
    score_k: accuracy score calculated on predictions with n = k. The number of scores
        calculated in this way depends on the value of max_actions_seen. For example,
        with max_actions_seen = 5, we will have score_0, score_1, ..., score_5.
    support_k: the number of predictions that support the corresponding score_k.
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}
"""

# 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 MetricaTesi(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("int32"),
                    "references": datasets.Value("int32"),
                    "actions_seen": datasets.Value("int32"),
                }
            ),
            # 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, actions_seen, max_actions_seen=20):
        """Returns the scores"""
        results = dict()
        for i in range(max_actions_seen + 1):
            score = 0.0
            support = sum(n == i for n in actions_seen)
            if support != 0:
                for prediction, reference, n in zip(predictions, references, actions_seen):
                    if n == i:
                        if prediction == reference:
                            score += 1
                score /= support
            if support != 0:
                results[f"support_{i}"] = support
                results[f"score_{i}"] = score
        return results