# -*- coding: utf-8 -*-
"""Utility function copied over from sklearn/base.py
"""
# Author: Yue Zhao <zhaoy@cmu.edu>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

import numpy as np
import six
from joblib.parallel import cpu_count


def _get_n_jobs(n_jobs):
    """Get number of jobs for the computation.
    See sklearn/utils/__init__.py for more information.

    This function reimplements the logic of joblib to determine the actual
    number of jobs depending on the cpu count. If -1 all CPUs are used.
    If 1 is given, no parallel computing code is used at all, which is useful
    for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
    Thus for n_jobs = -2, all CPUs but one are used.
    Parameters
    ----------
    n_jobs : int
        Number of jobs stated in joblib convention.
    Returns
    -------
    n_jobs : int
        The actual number of jobs as positive integer.
    """
    if n_jobs < 0:
        return max(cpu_count() + 1 + n_jobs, 1)
    elif n_jobs == 0:
        raise ValueError('Parameter n_jobs == 0 has no meaning.')
    else:
        return n_jobs


def _partition_estimators(n_estimators, n_jobs):
    """Private function used to partition estimators between jobs.
    See sklearn/ensemble/base.py for more information.
    """
    # Compute the number of jobs
    n_jobs = min(_get_n_jobs(n_jobs), n_estimators)

    # Partition estimators between jobs
    n_estimators_per_job = (n_estimators // n_jobs) * np.ones(n_jobs,
                                                              dtype=np.int)
    n_estimators_per_job[:n_estimators % n_jobs] += 1
    starts = np.cumsum(n_estimators_per_job)

    return n_jobs, n_estimators_per_job.tolist(), [0] + starts.tolist()


def _pprint(params, offset=0, printer=repr):
    # noinspection PyPep8
    """Pretty print the dictionary 'params'

    See http://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
    and sklearn/base.py for more information.

    :param params: The dictionary to pretty print
    :type params: dict

    :param offset: The offset in characters to add at the begin of each line.
    :type offset: int

    :param printer: The function to convert entries to strings, typically
        the builtin str or repr
    :type printer: callable

    :return: None
    """

    # Do a multi-line justified repr:
    options = np.get_printoptions()
    np.set_printoptions(precision=5, threshold=64, edgeitems=2)
    params_list = list()
    this_line_length = offset
    line_sep = ',\n' + (1 + offset // 2) * ' '
    for i, (k, v) in enumerate(sorted(six.iteritems(params))):
        if type(v) is float:
            # use str for representing floating point numbers
            # this way we get consistent representation across
            # architectures and versions.
            this_repr = '%s=%s' % (k, str(v))
        else:
            # use repr of the rest
            this_repr = '%s=%s' % (k, printer(v))
        if len(this_repr) > 500:
            this_repr = this_repr[:300] + '...' + this_repr[-100:]
        if i > 0:
            if this_line_length + len(this_repr) >= 75 or '\n' in this_repr:
                params_list.append(line_sep)
                this_line_length = len(line_sep)
            else:
                params_list.append(', ')
                this_line_length += 2
        params_list.append(this_repr)
        this_line_length += len(this_repr)

    np.set_printoptions(**options)
    lines = ''.join(params_list)
    # Strip trailing space to avoid nightmare in doctests
    lines = '\n'.join(l.rstrip(' ') for l in lines.split('\n'))
    return lines
