# Copyright The 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.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Sequence, Union

import torch
from torch import Tensor

from torchmetrics import Metric
from torchmetrics.functional.text.squad import (
    PREDS_TYPE,
    TARGETS_TYPE,
    _squad_compute,
    _squad_input_check,
    _squad_update,
)
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE

if not _MATPLOTLIB_AVAILABLE:
    __doctest_skip__ = ["SQuAD.plot"]


class SQuAD(Metric):
    """Calculate `SQuAD Metric`_ which is a metric for evaluating question answering models.

    This metric corresponds to the scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

    As input to ``forward`` and ``update`` the metric accepts the following input:

    -  ``preds`` (:class:`~Dict`): A Dictionary or List of Dictionary-s that map ``id`` and ``prediction_text`` to
       the respective values

       Example ``prediction``:

                .. code-block:: python

                    {"prediction_text": "TorchMetrics is awesome", "id": "123"}


    - ``target`` (:class:`~Dict`): A Dictionary or List of Dictionary-s that contain the ``answers`` and ``id`` in
      the SQuAD Format.

        Example ``target``:

        .. code-block:: python

            {
                'answers': [{'answer_start': [1], 'text': ['This is a test answer']}],
                'id': '1',
            }

        Reference SQuAD Format:

        .. code-block:: python

            {
                'answers': {'answer_start': [1], 'text': ['This is a test text']},
                'context': 'This is a test context.',
                'id': '1',
                'question': 'Is this a test?',
                'title': 'train test'
            }

    As output of ``forward`` and ``compute`` the metric returns the following output:

    -  ``squad`` (:class:`~Dict`): A dictionary containing the F1 score (key: "f1"),
        and Exact match score (key: "exact_match") for the batch.

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

    Example:
        >>> from torchmetrics.text import SQuAD
        >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
        >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
        >>> squad = SQuAD()
        >>> squad(preds, target)
        {'exact_match': tensor(100.), 'f1': tensor(100.)}

    """

    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False
    plot_lower_bound: float = 0.0
    plot_upper_bound: float = 100.0

    f1_score: Tensor
    exact_match: Tensor
    total: Tensor

    def __init__(
        self,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)

        self.add_state(name="f1_score", default=torch.tensor(0, dtype=torch.float), dist_reduce_fx="sum")
        self.add_state(name="exact_match", default=torch.tensor(0, dtype=torch.float), dist_reduce_fx="sum")
        self.add_state(name="total", default=torch.tensor(0, dtype=torch.int), dist_reduce_fx="sum")

    def update(self, preds: PREDS_TYPE, target: TARGETS_TYPE) -> None:
        """Update state with predictions and targets."""
        preds_dict, target_dict = _squad_input_check(preds, target)
        f1_score, exact_match, total = _squad_update(preds_dict, target_dict)
        self.f1_score += f1_score
        self.exact_match += exact_match
        self.total += total

    def compute(self) -> Dict[str, Tensor]:
        """Aggregate the F1 Score and Exact match for the batch."""
        return _squad_compute(self.f1_score, self.exact_match, self.total)

    def plot(
        self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
    ) -> _PLOT_OUT_TYPE:
        """Plot a single or multiple values from the metric.

        Args:
            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
                If no value is provided, will automatically call `metric.compute` and plot that result.
            ax: An matplotlib axis object. If provided will add plot to that axis

        Returns:
            Figure and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> # Example plotting a single value
            >>> from torchmetrics.text import SQuAD
            >>> metric = SQuAD()
            >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
            >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
            >>> metric.update(preds, target)
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> from torchmetrics.text import SQuAD
            >>> metric = SQuAD()
            >>> preds = [{"prediction_text": "1976", "id": "56e10a3be3433e1400422b22"}]
            >>> target = [{"answers": {"answer_start": [97], "text": ["1976"]}, "id": "56e10a3be3433e1400422b22"}]
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(preds, target))
            >>> fig_, ax_ = metric.plot(values)

        """
        return self._plot(val, ax)
