# Copyright 2019 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
# limitations under the License.
# ==============================================================================
import math
from typing import Any, Dict, Iterable, List, Optional, TypeVar, Union

import numpy as np
import tensorflow as tf
import torch
from scipy.linalg import hadamard

from fastestimator.backend._expand_dims import expand_dims
from fastestimator.backend._iwd import iwd
from fastestimator.backend._reduce_sum import reduce_sum
from fastestimator.backend._to_tensor import to_tensor
from fastestimator.op.tensorop.tensorop import TensorOp
from fastestimator.util.traceability_util import traceable

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


@traceable()
class UnHadamard(TensorOp):
    """Convert hadamard encoded class representations into onehot probabilities.

    Args:
        inputs: Key of the input tensor(s) to be converted.
        outputs: Key of the output tensor(s) as class probabilities.
        n_classes: How many classes are there in the inputs.
        code_length: What code length to use. Will default to the smallest power of 2 which is >= the number of classes.
        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, List[str]],
                 outputs: Union[str, List[str]],
                 n_classes: int,
                 code_length: Optional[int] = None,
                 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.in_list, self.out_list = True, True
        self.n_classes = n_classes
        if code_length is None:
            code_length = 1 << (n_classes - 1).bit_length()
        if code_length <= 0 or (code_length & (code_length - 1) != 0):
            raise ValueError(f"code_length must be a positive power of 2, but got {code_length}.")
        if code_length < n_classes:
            raise ValueError(f"code_length must be >= n_classes, but got {code_length} and {n_classes}")
        self.code_length = code_length
        self.labels = None
        self.eps = None

    def build(self, framework: str, device: Optional[torch.device] = None) -> None:
        labels = hadamard(self.code_length).astype(np.float32)
        labels[np.arange(0, self.code_length, 2), 0] = -1  # Make first column alternate
        labels = labels[:self.n_classes]
        self.labels = to_tensor(labels, target_type=framework)
        max_prob = 0.99999  # This will only be approximate since the first column is alternating
        power = 1.0
        self.eps = to_tensor(
            np.array((self.code_length + 1) * math.pow((1.0 - max_prob) / (max_prob * (self.n_classes - 1)), 1 / power),
                     dtype=np.float32),
            target_type=framework)
        if framework == "torch":
            self.labels = self.labels.to(device)
            self.eps = self.eps.to(device)

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> List[Tensor]:
        results = []
        for elem in data:
            # L1 Distance
            x = reduce_sum(abs(expand_dims(elem, axis=1) - self.labels), axis=-1)
            x = iwd(x, power=1.0, eps=self.eps)
            results.append(x)
        return results
