#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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.
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import numpy as np

from ... import opcodes as OperandDef
from ..utils import infer_dtype
from .core import TensorUnaryOp
from .utils import arithmetic_operand


@arithmetic_operand(sparse_mode="unary")
class TensorLog2(TensorUnaryOp):
    _op_type_ = OperandDef.LOG2
    _func_name = "log2"


@infer_dtype(np.log2)
def log2(x, out=None, where=None, **kwargs):
    """
    Base-2 logarithm of `x`.

    Parameters
    ----------
    x : array_like
        Input values.
    out : Tensor, None, or tuple of tensor and None, optional
        A location into which the result is stored. If provided, it must have
        a shape that the inputs broadcast to. If not provided or `None`,
        a freshly-allocated tensor is returned. A tuple (possible only as a
        keyword argument) must have length equal to the number of outputs.
    where : array_like, optional
        Values of True indicate to calculate the ufunc at that position, values
        of False indicate to leave the value in the output alone.
    **kwargs

    Returns
    -------
    y : Tensor
        Base-2 logarithm of `x`.

    See Also
    --------
    log, log10, log1p

    Logarithm is a multivalued function: for each `x` there is an infinite
    number of `z` such that `2**z = x`. The convention is to return the `z`
    whose imaginary part lies in `[-pi, pi]`.

    For real-valued input data types, `log2` always returns real output.
    For each value that cannot be expressed as a real number or infinity,
    it yields ``nan`` and sets the `invalid` floating point error flag.

    For complex-valued input, `log2` is a complex analytical function that
    has a branch cut `[-inf, 0]` and is continuous from above on it. `log2`
    handles the floating-point negative zero as an infinitesimal negative
    number, conforming to the C99 standard.

    Examples
    --------
    >>> import mars.tensor as mt

    >>> x = mt.array([0, 1, 2, 2**4])
    >>> mt.log2(x).execute()
    array([-Inf,   0.,   1.,   4.])

    >>> xi = mt.array([0+1.j, 1, 2+0.j, 4.j])
    >>> mt.log2(xi).execute()
    array([ 0.+2.26618007j,  0.+0.j        ,  1.+0.j        ,  2.+2.26618007j])
    """
    op = TensorLog2(**kwargs)
    return op(x, out=out, where=where)
