# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Optional

from torch import Tensor, tensor

from torchmetrics.functional.retrieval.precision import retrieval_precision
from torchmetrics.retrieval.base import RetrievalMetric


class RetrievalPrecision(RetrievalMetric):
    """Computes `IR Precision`_.

    Works with binary target data. Accepts float predictions from a model output.

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

    - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``
    - ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)``
    - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a
      prediction belongs

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

    - ``p2`` (:class:`~torch.Tensor`): A single-value tensor with the precision (at ``k``) of the predictions
      ``preds`` w.r.t. the labels ``target``

    All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning,
    so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by
    ``indexes`` and then will be computed as the mean of the metric over each query.

    Args:
        empty_target_action:
            Specify what to do with queries that do not have at least a positive ``target``. Choose from:

            - ``'neg'``: those queries count as ``0.0`` (default)
            - ``'pos'``: those queries count as ``1.0``
            - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned
            - ``'error'``: raise a ``ValueError``

        ignore_index:
            Ignore predictions where the target is equal to this number.
        k: consider only the top k elements for each query (default: ``None``, which considers them all)
        adaptive_k: adjust ``k`` to ``min(k, number of documents)`` for each query
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Raises:
        ValueError:
            If ``empty_target_action`` is not one of ``error``, ``skip``, ``neg`` or ``pos``.
        ValueError:
            If ``ignore_index`` is not `None` or an integer.
        ValueError:
            If ``k`` is not `None` or an integer larger than 0.
        ValueError:
            If ``adaptive_k`` is not boolean.

    Example:
        >>> from torchmetrics import RetrievalPrecision
        >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])
        >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])
        >>> target = tensor([False, False, True, False, True, False, True])
        >>> p2 = RetrievalPrecision(k=2)
        >>> p2(preds, target, indexes=indexes)
        tensor(0.5000)
    """

    is_differentiable: bool = False
    higher_is_better: bool = True
    full_state_update: bool = False

    def __init__(
        self,
        empty_target_action: str = "neg",
        ignore_index: Optional[int] = None,
        k: Optional[int] = None,
        adaptive_k: bool = False,
        **kwargs: Any,
    ) -> None:
        super().__init__(
            empty_target_action=empty_target_action,
            ignore_index=ignore_index,
            **kwargs,
        )

        if (k is not None) and not (isinstance(k, int) and k > 0):
            raise ValueError("`k` has to be a positive integer or None")
        if not isinstance(adaptive_k, bool):
            raise ValueError("`adaptive_k` has to be a boolean")
        self.k = k
        self.adaptive_k = adaptive_k

    def _metric(self, preds: Tensor, target: Tensor) -> Tensor:
        return retrieval_precision(preds, target, k=self.k, adaptive_k=self.adaptive_k)