# Copyright The PyTorch Lightning team.
#
# 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.
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# limitations under the License.

from typing import Any, Dict, List, Optional, Union

from torch import Tensor, tensor

from torchmetrics.functional.text.wil import _wil_compute, _wil_update
from torchmetrics.metric import Metric


class WordInfoLost(Metric):
    r"""Word Information Lost (WIL_) is a metric of the performance of an automatic speech
    recognition system. This value indicates the percentage of words that were incorrectly predicted
    between a set of ground-truth sentences and a set of hypothesis sentences.
    The lower the value, the better the performance of the ASR system with a WordInfoLost of
    0 being a perfect score.
    Word Information Lost rate can then be computed as:

    .. math::
        wil = 1 - \frac{C}{N} + \frac{C}{P}

    where:

        - :math:`C` is the number of correct words,
        - :math:`N` is the number of words in the reference
        - :math:`P` is the number of words in the prediction


    Args:
        compute_on_step:
            Forward only calls ``update()`` and returns None if this is set to False.

            .. deprecated:: v0.8
                Argument has no use anymore and will be removed v0.9.

        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.


    Examples:
        >>> from torchmetrics import WordInfoLost
        >>> preds = ["this is the prediction", "there is an other sample"]
        >>> target = ["this is the reference", "there is another one"]
        >>> metric = WordInfoLost()
        >>> metric(preds, target)
        tensor(0.6528)
    """
    is_differentiable = False
    higher_is_better = False
    errors: Tensor
    target_total: Tensor
    preds_total: Tensor

    def __init__(
        self,
        compute_on_step: Optional[bool] = None,
        **kwargs: Dict[str, Any],
    ):
        super().__init__(compute_on_step=compute_on_step, **kwargs)
        self.add_state("errors", tensor(0.0), dist_reduce_fx="sum")
        self.add_state("target_total", tensor(0.0), dist_reduce_fx="sum")
        self.add_state("preds_total", tensor(0.0), dist_reduce_fx="sum")

    def update(self, preds: Union[str, List[str]], target: Union[str, List[str]]) -> None:  # type: ignore
        """Store predictions/references for computing Word Information Lost scores.

        Args:
            preds: Transcription(s) to score as a string or list of strings
            target: Reference(s) for each speech input as a string or list of strings
        """
        errors, target_total, preds_total = _wil_update(preds, target)
        self.errors += errors
        self.target_total += target_total
        self.preds_total += preds_total

    def compute(self) -> Tensor:
        """Calculate the Word Information Lost.

        Returns:
            Word Information Lost score
        """
        return _wil_compute(self.errors, self.target_total, self.preds_total)
