#===============================================================================
# Copyright 2014 Intel Corporation
#
# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
#===============================================================================

from daal4py.sklearn._utils import set_idp_sklearn_verbose
from ..neighbors import KNeighborsRegressor as KNeighborsRegressor_daal4py
from ..neighbors import NearestNeighbors as NearestNeighbors_daal4py
from ..neighbors import KNeighborsClassifier as KNeighborsClassifier_daal4py
from ..model_selection import train_test_split
from ..utils.validation import _assert_all_finite
from ..svm.svm import SVC as SVC_daal4py
from ..ensemble._forest import RandomForestClassifier as RandomForestClassifier_daal4py
from ..ensemble._forest import RandomForestRegressor as RandomForestRegressor_daal4py
from ..metrics import roc_auc_score
from ..metrics import pairwise_distances
from ..cluster.k_means import KMeans as KMeans_daal4py
from ..cluster.dbscan import DBSCAN as DBSCAN_daal4py
from ..linear_model.coordinate_descent import Lasso as Lasso_daal4py
from ..linear_model.coordinate_descent import ElasticNet as ElasticNet_daal4py
from ..linear_model.linear import LinearRegression as LinearRegression_daal4py
from ..linear_model.ridge import Ridge as Ridge_daal4py
from ..linear_model.logistic_path import LogisticRegression as LogisticRegression_daal4py
from ..linear_model.logistic_path import logistic_regression_path as \
    daal_optimized_logistic_path
from ..decomposition._pca import PCA as PCA_daal4py
from ..manifold import TSNE as TSNE_daal4py
from sklearn import model_selection
from sklearn import metrics
from sklearn.utils import validation
import sys
from functools import lru_cache

import sklearn.cluster as cluster_module
import sklearn.ensemble as ensemble_module
import sklearn.svm as svm_module
import sklearn.linear_model._logistic as logistic_module
import sklearn.neighbors as neighbors_module
import sklearn.decomposition as decomposition_module
import sklearn.linear_model as linear_model_module
import sklearn.manifold as manifold_module

import warnings


@lru_cache(maxsize=None)
def _get_map_of_algorithms():
    mapping = {
        'pca': [[(decomposition_module, 'PCA', PCA_daal4py), None]],
        'kmeans': [[(cluster_module, 'KMeans', KMeans_daal4py), None]],
        'dbscan': [[(cluster_module, 'DBSCAN', DBSCAN_daal4py), None]],
        'distances': [[(metrics, 'pairwise_distances', pairwise_distances), None]],
        'linear': [[(linear_model_module, 'LinearRegression',
                     LinearRegression_daal4py), None]],
        'ridge': [[(linear_model_module, 'Ridge', Ridge_daal4py), None]],
        'elasticnet': [[(linear_model_module, 'ElasticNet', ElasticNet_daal4py), None]],
        'lasso': [[(linear_model_module, 'Lasso', Lasso_daal4py), None]],
        'svm': [[(svm_module, 'SVC', SVC_daal4py), None]],
        'logistic': [[(logistic_module, '_logistic_regression_path',
                       daal_optimized_logistic_path), None]],
        'log_reg': [[(linear_model_module, 'LogisticRegression',
                    LogisticRegression_daal4py), None]],
        'knn_classifier': [[(neighbors_module, 'KNeighborsClassifier',
                             KNeighborsClassifier_daal4py), None]],
        'nearest_neighbors': [[(neighbors_module, 'NearestNeighbors',
                                NearestNeighbors_daal4py), None]],
        'knn_regressor': [[(neighbors_module, 'KNeighborsRegressor',
                            KNeighborsRegressor_daal4py), None]],
        'random_forest_classifier': [[(ensemble_module, 'RandomForestClassifier',
                                       RandomForestClassifier_daal4py), None]],
        'random_forest_regressor': [[(ensemble_module, 'RandomForestRegressor',
                                      RandomForestRegressor_daal4py), None]],
        'train_test_split': [[(model_selection, 'train_test_split',
                               train_test_split), None]],
        'fin_check': [[(validation, '_assert_all_finite',
                        _assert_all_finite), None]],
        'roc_auc_score': [[(metrics, 'roc_auc_score',
                           roc_auc_score), None]],
        'tsne': [[(manifold_module, 'TSNE', TSNE_daal4py), None]],
    }
    mapping['svc'] = mapping['svm']
    mapping['logisticregression'] = mapping['log_reg']
    mapping['kneighborsclassifier'] = mapping['knn_classifier']
    mapping['nearestneighbors'] = mapping['nearest_neighbors']
    mapping['kneighborsregressor'] = mapping['knn_regressor']
    mapping['randomforestclassifier'] = mapping['random_forest_classifier']
    mapping['randomforestregressor'] = mapping['random_forest_regressor']
    mapping["linearregression"] = mapping["linear"]
    mapping["logisticregression"] = mapping["log_reg"]
    mapping["_logistic_regression_path"] = mapping["logistic"]
    mapping["_assert_all_finite"] = mapping["fin_check"]
    mapping["pairwise_distances"] = mapping["distances"]
    return mapping


def do_patch(name, get_map=_get_map_of_algorithms):
    lname = name.lower()
    if lname in get_map():
        for descriptor in get_map()[lname]:
            which, what, replacer = descriptor[0]
            if descriptor[1] is None:
                descriptor[1] = getattr(which, what, None)
            setattr(which, what, replacer)
    else:
        raise ValueError("Has no patch for: " + name)


def do_unpatch(name, get_map=_get_map_of_algorithms):
    lname = name.lower()
    if lname in get_map():
        for descriptor in get_map()[lname]:
            if descriptor[1] is not None:
                which, what, _ = descriptor[0]
                setattr(which, what, descriptor[1])
    else:
        raise ValueError("Has no patch for: " + name)


def enable(name=None, verbose=True, deprecation=True, get_map=_get_map_of_algorithms):
    if name is not None:
        do_patch(name, get_map)
    else:
        for key in get_map():
            do_patch(key, get_map)
    if deprecation:
        set_idp_sklearn_verbose()
        warnings.warn_explicit("\nScikit-learn patching with daal4py is deprecated "
                               "and will be removed in the future.\n"
                               "Use Intel(R) Extension "
                               "for Scikit-learn* module instead "
                               "(pip install scikit-learn-intelex).\n"
                               "To enable patching, please use one of the "
                               "following options:\n"
                               "1) From the command line:\n"
                               "    python -m sklearnex <your_script>\n"
                               "2) From your script:\n"
                               "    from sklearnex import patch_sklearn\n"
                               "    patch_sklearn()",
                               FutureWarning, "dispatcher.py", 151)
    if verbose and deprecation and sys.stderr is not None:
        sys.stderr.write(
            "Intel(R) oneAPI Data Analytics Library solvers for sklearn enabled: "
            "https://intelpython.github.io/daal4py/sklearn.html\n")


def disable(name=None, get_map=_get_map_of_algorithms):
    if name is not None:
        do_unpatch(name, get_map)
    else:
        for key in get_map():
            do_unpatch(key, get_map)
        get_map.cache_clear()


def _is_enabled(name, get_map=_get_map_of_algorithms):
    lname = name.lower()
    if lname in get_map():
        enabled = True
        for descriptor in get_map()[lname]:
            which, what, replacer = descriptor[0]
            enabled = enabled and getattr(which, what) == replacer
        return enabled
    else:
        raise ValueError("Has no patch for: " + name)


def patch_is_enabled(name=None, get_map=_get_map_of_algorithms, return_map=False):
    if name is not None:
        return _is_enabled(name, get_map)
    else:
        if return_map:
            enabled = {}
            for key in get_map():
                enabled[key] = _is_enabled(key, get_map)
        else:
            enabled = True
            for key in get_map():
                enabled = enabled and _is_enabled(key, get_map)
        return enabled


def _patch_names():
    return list(_get_map_of_algorithms().keys())
