# 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 operator import eq
from typing import Callable, Iterable, Union

import evaluate
import datasets
import numpy as np
import logging

logger = logging.getLogger(__name__)


# 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 = """\
Computes precision, recall, f1 scores for joint entity-relation extraction task.
"""


# 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.
    eq_fn: function to compare two items. Defaults to the equality operator.
Returns:
    recall: 
    precision:
    f1:
Examples:
    >>> jer = evaluate.load("jer")
    >>> results = jer.compute(references=[["Baris | play | tennis", "Deniz | travel | London"]], predictions=[["Baris | play | tennis"]])
    >>> print(results)
    {'recall': 0.5, 'precision': 1.0, 'f1': 0.6666666666666666}
"""

Triplet = Union[str, tuple, int]

@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class jer(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.features.Sequence(datasets.Value('string')),
                'references': datasets.features.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"""
        pass

    def _compute(self, predictions, references, eq_fn=eq):
        """Returns the scores"""
        score_dicts = [
            self._compute_single(prediction=prediction, reference=reference, eq_fn=eq_fn) 
            for prediction, reference in zip(predictions, references)
        ]
        return {('mean_' + key): np.mean([scores[key] for scores in score_dicts]) for key in score_dicts[0].keys()}
    
    def _compute_single(
            self, 
            *, 
            prediction: Iterable[Triplet], 
            reference: Iterable[Triplet], 
            eq_fn: Callable[[Triplet, Triplet], bool],
        ):
        reference_set = set(reference)
        if len(reference) != len(reference_set):
            logger.warn(f"Duplicates found in the reference list {reference}")
        prediction_set = set(prediction)

        tp = sum(int(is_in(item, prediction, eq_fn=eq_fn)) for item in reference)
        fp = len(prediction_set) - tp
        fn = len(reference_set) - tp
        
        # Calculate metrics
        precision = tp / (tp + fp) if tp + fp > 0 else 0
        recall = tp / (tp + fn) if tp + fn > 0 else 0
        f1_score = 2 * (precision * recall) / (precision + recall) if precision + recall > 0 else 0
        
        return {
            'precision': precision,
            'recall': recall,
            'f1': f1_score
        }

def is_in(target, collection: Iterable, eq_fn=eq) -> bool:
    for item in collection:
        if eq_fn(item, target):
            return True
    return False