"""Elementwise operators"""
from __future__ import absolute_import as _abs
import tvm
from . import tag

@tvm.tag_scope(tag=tag.ELEMWISE)
def identity(x):
    """Take identity of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    # pylint: disable=unnecessary-lambda
    return tvm.compute(x.shape, lambda *i: x(*i))


@tvm.tag_scope(tag=tag.ELEMWISE)
def negative(x):
    """Take negation of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    # pylint: disable=unnecessary-lambda
    return tvm.compute(x.shape, lambda *i: -x(*i))


@tvm.tag_scope(tag=tag.ELEMWISE)
def exp(x):
    """Take exponential of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: tvm.exp(x(*i)))


@tvm.tag_scope(tag=tag.ELEMWISE)
def tanh(x):
    """Take hyperbolic tanh of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: tvm.tanh(x(*i)))


@tvm.tag_scope(tag=tag.ELEMWISE)
def log(x):
    """Take logarithm of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: tvm.log(x(*i)))


@tvm.tag_scope(tag=tag.ELEMWISE)
def sqrt(x):
    """Take square root of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: tvm.sqrt(x(*i)))


@tvm.tag_scope(tag=tag.ELEMWISE)
def sigmoid(x):
    """Take sigmoid tanh of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: tvm.sigmoid(x(*i)))


@tvm.tag_scope(tag=tag.ELEMWISE)
def left_shift(x, n):
    """Take n bits left shift of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.
    n : int
        Number of bits.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: x(*i) << n)


@tvm.tag_scope(tag=tag.ELEMWISE)
def right_shift(x, n):
    """Take n bits right shift of input x.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.
    n : int
        Number of bits.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: x(*i) >> n)


@tvm.tag_scope(tag=tag.ELEMWISE)
def clip(x, a_min, a_max):
    """Clip (limit) the values in an array. Given an interval, values
    outside the interval are clipped to the interval edges.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.
    a_min : int or float
        Minimum value.
    a_max : int or float
        Maximum value.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    def _compute(*indices):
        value = x(*indices)
        const_min = tvm.const(a_min, value.dtype)
        const_max = tvm.const(a_max, value.dtype)
        return tvm.max(tvm.min(value, const_max), const_min)
    return tvm.compute(x.shape, _compute)


@tvm.tag_scope(tag=tag.ELEMWISE)
def cast(x, dtype):
    """Cast input to specified data type.

    Parameters
    ----------
    x : tvm.Tensor
        Input argument.
    dtype : str
        Data type.

    Returns
    -------
    y : tvm.Tensor
        The result.
    """
    return tvm.compute(x.shape, lambda *i: x(*i).astype(dtype))
