###################################################################

#  Numexpr - Fast numerical array expression evaluator for NumPy.

#

#      License: MIT

#      Author:  See AUTHORS.txt

#

#  See LICENSE.txt and LICENSES/*.txt for details about copyright and

#  rights to use.

####################################################################



import os

import subprocess



from numexpr.interpreter import _set_num_threads, MAX_THREADS

from numexpr import use_vml



if use_vml:

    from numexpr.interpreter import (

        _get_vml_version, _set_vml_accuracy_mode, _set_vml_num_threads)





def get_vml_version():

    """Get the VML/MKL library version."""

    if use_vml:

        return _get_vml_version()

    else:

        return None





def set_vml_accuracy_mode(mode):

    """

    Set the accuracy mode for VML operations.



    The `mode` parameter can take the values:

    - 'high': high accuracy mode (HA), <1 least significant bit

    - 'low': low accuracy mode (LA), typically 1-2 least significant bits

    - 'fast': enhanced performance mode (EP)

    - None: mode settings are ignored



    This call is equivalent to the `vmlSetMode()` in the VML library.

    See:



    http://www.intel.com/software/products/mkl/docs/webhelp/vml/vml_DataTypesAccuracyModes.html



    for more info on the accuracy modes.



    Returns old accuracy settings.

    """

    if use_vml:

        acc_dict = {None: 0, 'low': 1, 'high': 2, 'fast': 3}

        acc_reverse_dict = {1: 'low', 2: 'high', 3: 'fast'}

        if mode not in list(acc_dict.keys()):

            raise ValueError(

                "mode argument must be one of: None, 'high', 'low', 'fast'")

        retval = _set_vml_accuracy_mode(acc_dict.get(mode, 0))

        return acc_reverse_dict.get(retval)

    else:

        return None





def set_vml_num_threads(nthreads):

    """

    Suggests a maximum number of threads to be used in VML operations.



    This function is equivalent to the call

    `mkl_domain_set_num_threads(nthreads, MKL_DOMAIN_VML)` in the MKL

    library.  See:



    http://www.intel.com/software/products/mkl/docs/webhelp/support/functn_mkl_domain_set_num_threads.html



    for more info about it.

    """

    if use_vml:

        _set_vml_num_threads(nthreads)





def set_num_threads(nthreads):

    """

    Sets a number of threads to be used in operations.



    Returns the previous setting for the number of threads.



    During initialization time Numexpr sets this number to the number

    of detected cores in the system (see `detect_number_of_cores()`).



    If you are using Intel's VML, you may want to use

    `set_vml_num_threads(nthreads)` to perform the parallel job with

    VML instead.  However, you should get very similar performance

    with VML-optimized functions, and VML's parallelizer cannot deal

    with common expresions like `(x+1)*(x-2)`, while Numexpr's one

    can.

    """

    old_nthreads = _set_num_threads(nthreads)

    return old_nthreads





def detect_number_of_cores():

    """

    Detects the number of cores on a system. Cribbed from pp.

    """

    # Linux, Unix and MacOS:

    if hasattr(os, "sysconf"):

        if "SC_NPROCESSORS_ONLN" in os.sysconf_names:

            # Linux & Unix:

            ncpus = os.sysconf("SC_NPROCESSORS_ONLN")

            if isinstance(ncpus, int) and ncpus > 0:

                return ncpus

        else:  # OSX:

            return int(subprocess.check_output(["sysctl", "-n", "hw.ncpu"]))

    # Windows:

    if "NUMBER_OF_PROCESSORS" in os.environ:

        ncpus = int(os.environ["NUMBER_OF_PROCESSORS"]);

        if ncpus > 0:

            return ncpus

    return 1  # Default





def detect_number_of_threads():

    """

    If this is modified, please update the note in: https://github.com/pydata/numexpr/wiki/Numexpr-Users-Guide

    """

    try:

        nthreads = int(os.environ['NUMEXPR_NUM_THREADS'])

    except KeyError:

        nthreads = int(os.environ.get('OMP_NUM_THREADS', detect_number_of_cores()))

        # Check that we don't activate too many threads at the same time.

        # 8 seems a sensible value.

        if nthreads > 8:

            nthreads = 8

    # Check that we don't surpass the MAX_THREADS in interpreter.cpp

    if nthreads > MAX_THREADS:

        nthreads = MAX_THREADS

    return nthreads





class CacheDict(dict):

    """

    A dictionary that prevents itself from growing too much.

    """



    def __init__(self, maxentries):

        self.maxentries = maxentries

        super(CacheDict, self).__init__(self)



    def __setitem__(self, key, value):

        # Protection against growing the cache too much

        if len(self) > self.maxentries:

            # Remove a 10% of (arbitrary) elements from the cache

            entries_to_remove = self.maxentries // 10

            for k in list(self.keys())[:entries_to_remove]:

                super(CacheDict, self).__delitem__(k)

        super(CacheDict, self).__setitem__(key, value)



