"""
The :mod:`sklearn` module includes functions to configure global settings and
get information about the working environment.
"""

# Machine learning module for Python
# ==================================
#
# sklearn is a Python module integrating classical machine
# learning algorithms in the tightly-knit world of scientific Python
# packages (numpy, scipy, matplotlib).
#
# It aims to provide simple and efficient solutions to learning problems
# that are accessible to everybody and reusable in various contexts:
# machine-learning as a versatile tool for science and engineering.
#
# See https://scikit-learn.org for complete documentation.

import logging
import os
import random
import sys

from ._config import config_context, get_config, set_config

logger = logging.getLogger(__name__)


# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
#   X.Y.0   # For first release after an increment in Y
#   X.Y.Z   # For bugfix releases
#
# Admissible pre-release markers:
#   X.Y.ZaN   # Alpha release
#   X.Y.ZbN   # Beta release
#   X.Y.ZrcN  # Release Candidate
#   X.Y.Z     # Final release
#
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
#
__version__ = "1.5.0"


# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
# simultaneously. This can happen for instance when calling BLAS inside a
# prange. Setting the following environment variable allows multiple OpenMP
# libraries to be loaded. It should not degrade performances since we manually
# take care of potential over-subcription performance issues, in sections of
# the code where nested OpenMP loops can happen, by dynamically reconfiguring
# the inner OpenMP runtime to temporarily disable it while under the scope of
# the outer OpenMP parallel section.
os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "True")

# Workaround issue discovered in intel-openmp 2019.5:
# https://github.com/ContinuumIO/anaconda-issues/issues/11294
os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")

try:
    # This variable is injected in the __builtins__ by the build
    # process. It is used to enable importing subpackages of sklearn when
    # the binaries are not built
    # mypy error: Cannot determine type of '__SKLEARN_SETUP__'
    __SKLEARN_SETUP__  # type: ignore
except NameError:
    __SKLEARN_SETUP__ = False

if __SKLEARN_SETUP__:
    sys.stderr.write("Partial import of sklearn during the build process.\n")
    # We are not importing the rest of scikit-learn during the build
    # process, as it may not be compiled yet
else:
    # Import numpy, scipy to make sure that the BLAS libs are loaded before
    # creating the ThreadpoolController. They would be imported just after
    # when importing utils anyway. This makes it explicit and robust to changes
    # in utils.
    # (OpenMP is loaded by importing show_versions right after this block)
    import numpy  # noqa
    import scipy.linalg  # noqa
    from threadpoolctl import ThreadpoolController

    # `_distributor_init` allows distributors to run custom init code.
    # For instance, for the Windows wheel, this is used to pre-load the
    # vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
    # sub-folder.
    # It is necessary to do this prior to importing show_versions as the
    # later is linked to the OpenMP runtime to make it possible to introspect
    # it and importing it first would fail if the OpenMP dll cannot be found.
    from . import (
        __check_build,  # noqa: F401
        _distributor_init,  # noqa: F401
    )
    from .base import clone
    from .utils._show_versions import show_versions

    __all__ = [
        "calibration",
        "cluster",
        "covariance",
        "cross_decomposition",
        "datasets",
        "decomposition",
        "dummy",
        "ensemble",
        "exceptions",
        "experimental",
        "externals",
        "feature_extraction",
        "feature_selection",
        "gaussian_process",
        "inspection",
        "isotonic",
        "kernel_approximation",
        "kernel_ridge",
        "linear_model",
        "manifold",
        "metrics",
        "mixture",
        "model_selection",
        "multiclass",
        "multioutput",
        "naive_bayes",
        "neighbors",
        "neural_network",
        "pipeline",
        "preprocessing",
        "random_projection",
        "semi_supervised",
        "svm",
        "tree",
        "discriminant_analysis",
        "impute",
        "compose",
        # Non-modules:
        "clone",
        "get_config",
        "set_config",
        "config_context",
        "show_versions",
    ]

    _BUILT_WITH_MESON = False
    try:
        import sklearn._built_with_meson  # noqa: F401

        _BUILT_WITH_MESON = True
    except ModuleNotFoundError:
        pass

    # Set a global controller that can be used to locally limit the number of
    # threads without looping through all shared libraries every time.
    # This instantitation should not happen earlier because it needs all BLAS and
    # OpenMP libs to be loaded first.
    _threadpool_controller = ThreadpoolController()


def setup_module(module):
    """Fixture for the tests to assure globally controllable seeding of RNGs"""

    import numpy as np

    # Check if a random seed exists in the environment, if not create one.
    _random_seed = os.environ.get("SKLEARN_SEED", None)
    if _random_seed is None:
        _random_seed = np.random.uniform() * np.iinfo(np.int32).max
    _random_seed = int(_random_seed)
    print("I: Seeding RNGs with %r" % _random_seed)
    np.random.seed(_random_seed)
    random.seed(_random_seed)