# 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 string

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.

    >>> metric = evaluate.load("DarrenChensformer/eval_keyphrase")
    >>> results = metric.compute(references=[["Hello","World"]], predictions=[["hello","world"]])
    >>> print(results)
    {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'num_pred': 2.0, 'num_gold': 2.0}
"""

# 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 eval_keyphrase(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 _normalize_keyphrase(self, kp):

        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_punc(lower(kp)))

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

        macro_metrics = {'precision': [], 'recall': [], 'f1': [], 'num_pred': [], 'num_gold': []}

        for targets, preds in zip(references, predictions):
            if ignore_empty_label:
                targets = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in targets if len(self._normalize_keyphrase(tmp_key).strip()) != 0]
                preds = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in preds if len(self._normalize_keyphrase(tmp_key).strip()) != 0]
            else:
                targets = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in targets]
                preds = [self._normalize_keyphrase(tmp_key).strip() for tmp_key in preds]
            total_tgt_set = set(targets)
            total_preds = set(preds)
            if len(total_tgt_set) == 0: continue

            # get the total_correctly_matched indicators
            total_correctly_matched = len(total_preds & total_tgt_set)

            # macro metric calculating
            precision = total_correctly_matched / len(total_preds) if len(total_preds) else 0.0
            recall = total_correctly_matched / len(total_tgt_set)
            f1 = 2 * precision * recall / (precision + recall) if total_correctly_matched > 0 else 0.0
            macro_metrics['precision'].append(precision)
            macro_metrics['recall'].append(recall)
            macro_metrics['f1'].append(f1)
            macro_metrics['num_pred'].append(len(total_preds))
            macro_metrics['num_gold'].append(len(total_tgt_set))

        return { k: round(sum(v)/len(v), 4) for k, v in macro_metrics.items()}