# Copyright 2021 The HuggingFace Evaluate Authors.
#
# 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.
""" Word Error Ratio (WER) metric. """

import datasets
from jiwer import compute_measures

import evaluate

_KWARGS_DESCRIPTION = """
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = evaluate.load("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
"""


@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class WER(evaluate.Metric):
    def _info(self):
        return evaluate.MetricInfo(
            features=datasets.Features(
                {
                    "predictions": datasets.Value("string", id="sequence"),
                    "references": datasets.Value("string", id="sequence"),
                }
            ),
            codebase_urls=["https://github.com/jitsi/jiwer/"],
            reference_urls=[
                "https://en.wikipedia.org/wiki/Word_error_rate",
            ],
        )

    def _compute(self, predictions=None, references=None, concatenate_texts=False):
        if concatenate_texts:
            return compute_measures(references, predictions)["wer"]
        else:
            incorrect = 0
            total = 0
            for prediction, reference in zip(predictions, references):
                measures = compute_measures(reference, prediction)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total