# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple

import torch


METRIC_EPS = 1e-6


def dim_zero_cat(x):
    return torch.cat(x, dim=0)


def dim_zero_sum(x):
    return torch.sum(x, dim=0)


def dim_zero_mean(x):
    return torch.mean(x, dim=0)


def _flatten(x):
    return [item for sublist in x for item in sublist]


def to_onehot(
        tensor: torch.Tensor,
        num_classes: int,
) -> torch.Tensor:
    """
    Converts a dense label tensor to one-hot format

    Args:
        tensor: dense label tensor, with shape [N, d1, d2, ...]
        num_classes: number of classes C

    Output:
        A sparse label tensor with shape [N, C, d1, d2, ...]

    Example:
        >>> x = torch.tensor([1, 2, 3])
        >>> to_onehot(x, num_classes=4)
        tensor([[0, 1, 0, 0],
                [0, 0, 1, 0],
                [0, 0, 0, 1]])
    """
    dtype, device, shape = tensor.dtype, tensor.device, tensor.shape
    tensor_onehot = torch.zeros(shape[0], num_classes, *shape[1:],
                                dtype=dtype, device=device)
    index = tensor.long().unsqueeze(1).expand_as(tensor_onehot)
    return tensor_onehot.scatter_(1, index, 1.0)


def _check_same_shape(pred: torch.Tensor, target: torch.Tensor):
    """ Check that predictions and target have the same shape, else raise error """
    if pred.shape != target.shape:
        raise RuntimeError('Predictions and targets are expected to have the same shape')


def _input_format_classification(
        preds: torch.Tensor,
        target: torch.Tensor,
        threshold: float = 0.5
) -> Tuple[torch.Tensor, torch.Tensor]:
    """ Convert preds and target tensors into label tensors

    Args:
        preds: either tensor with labels, tensor with probabilities/logits or
            multilabel tensor
        target: tensor with ground true labels
        threshold: float used for thresholding multilabel input

    Returns:
        preds: tensor with labels
        target: tensor with labels
    """
    if not (len(preds.shape) == len(target.shape) or len(preds.shape) == len(target.shape) + 1):
        raise ValueError(
            "preds and target must have same number of dimensions, or one additional dimension for preds"
        )

    if len(preds.shape) == len(target.shape) + 1:
        # multi class probabilites
        preds = torch.argmax(preds, dim=1)

    if len(preds.shape) == len(target.shape) and preds.dtype == torch.float:
        # binary or multilabel probablities
        preds = (preds >= threshold).long()
    return preds, target


def _input_format_classification_one_hot(
        num_classes: int,
        preds: torch.Tensor,
        target: torch.Tensor,
        threshold: float = 0.5,
        multilabel: bool = False
) -> Tuple[torch.Tensor, torch.Tensor]:
    """ Convert preds and target tensors into one hot spare label tensors

    Args:
        num_classes: number of classes
        preds: either tensor with labels, tensor with probabilities/logits or
            multilabel tensor
        target: tensor with ground true labels
        threshold: float used for thresholding multilabel input
        multilabel: boolean flag indicating if input is multilabel

    Returns:
        preds: one hot tensor of shape [num_classes, -1] with predicted labels
        target: one hot tensors of shape [num_classes, -1] with true labels
    """
    if not (len(preds.shape) == len(target.shape) or len(preds.shape) == len(target.shape) + 1):
        raise ValueError(
            "preds and target must have same number of dimensions, or one additional dimension for preds"
        )

    if len(preds.shape) == len(target.shape) + 1:
        # multi class probabilites
        preds = torch.argmax(preds, dim=1)

    if len(preds.shape) == len(target.shape) and preds.dtype == torch.long and num_classes > 1 and not multilabel:
        # multi-class
        preds = to_onehot(preds, num_classes=num_classes)
        target = to_onehot(target, num_classes=num_classes)

    elif len(preds.shape) == len(target.shape) and preds.dtype == torch.float:
        # binary or multilabel probablities
        preds = (preds >= threshold).long()

    # transpose class as first dim and reshape
    if len(preds.shape) > 1:
        preds = preds.transpose(1, 0)
        target = target.transpose(1, 0)

    return preds.reshape(num_classes, -1), target.reshape(num_classes, -1)