# Copyright 2022 The FastEstimator Authors. All Rights Reserved.
#
# 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
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# ==============================================================================
from typing import Any, Dict, Iterable, List, Sequence, TypeVar, Union

import numpy as np
import tensorflow as tf
import torch

from fastestimator.backend._permute import permute
from fastestimator.op.tensorop.tensorop import TensorOp
from fastestimator.util.traceability_util import traceable

Tensor = TypeVar('Tensor', tf.Tensor, torch.Tensor, np.ndarray)


@traceable()
class Permute(TensorOp):
    """Permute a input tensor.

    Args:
        inputs: Key of the input tensor that is to be normalized.
        outputs: Key of the output tensor that has been normalized.
        permutation: Sequence[int]
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str]],
                 outputs: Union[str, Iterable[str]],
                 permutation: Sequence[int] = (0, 3, 1, 2),
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None) -> None:
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
        self.permutation = permutation

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Union[Tensor, List[Tensor]]:
        return permute(data, self.permutation)
