#!/usr/bin/env python
#===============================================================================
# Copyright 2023 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.
#===============================================================================

import numpy as np
import numbers
from math import sqrt
from scipy.sparse import issparse

from ..._device_offload import dispatch
from daal4py.sklearn._utils import sklearn_check_version

from sklearn.utils.extmath import stable_cumsum
from onedal.datatypes import _check_array
from sklearn.utils.validation import check_array
from sklearn.base import BaseEstimator
from sklearn.utils.validation import check_is_fitted
if sklearn_check_version('1.1') and not sklearn_check_version('1.2'):
    from sklearn.utils import check_scalar
if sklearn_check_version('0.23'):
    from sklearn.decomposition._pca import _infer_dimension
else:
    from sklearn.decomposition._pca import _infer_dimension_

from onedal.decomposition import PCA as onedal_PCA
from sklearn.decomposition import PCA as sklearn_PCA


class PCA(sklearn_PCA):
    if sklearn_check_version('1.2'):
        _parameter_constraints: dict = {**sklearn_PCA._parameter_constraints}

    def __init__(
        self,
        n_components=None,
        *,
        copy=True,
        whiten=False,
        svd_solver="auto",
        tol=0.0,
        iterated_power="auto",
        n_oversamples=10,
        power_iteration_normalizer="auto",
        random_state=None,
    ):
        self.n_components = n_components
        self.copy = copy
        self.whiten = whiten
        self.svd_solver = svd_solver
        self.tol = tol
        self.iterated_power = iterated_power
        self.n_oversamples = n_oversamples
        self.power_iteration_normalizer = power_iteration_normalizer
        self.random_state = random_state

    def _validate_n_components(self, n_components, n_samples,
                               n_features, n_sf_min):
        if n_components == "mle":
            if n_samples < n_features:
                raise ValueError(
                    "n_components='mle' is only supported if"
                    " n_samples >= n_features"
                )
        elif not 0 <= n_components <= n_sf_min:
            raise ValueError(
                "n_components=%r must be between 0 and "
                "min(n_samples, n_features)=%r with "
                "svd_solver='full'" % (n_components, min(n_samples, n_features))
            )
        elif n_components >= 1:
            if not isinstance(n_components, numbers.Integral):
                raise ValueError("n_components=%r must be of type int "
                                 "when greater than or equal to 1, "
                                 "was of type=%r"
                                 % (n_components, type(n_components)))

    def fit(self, X, y=None):
        if sklearn_check_version('1.2'):
            self._validate_params()
        elif sklearn_check_version('1.1'):
            check_scalar(
                self.n_oversamples,
                "n_oversamples",
                min_val=1,
                target_type=numbers.Integral,
            )
        self._fit(X)
        return self

    def _fit(self, X):
        if issparse(X):
            raise TypeError(
                "PCA does not support sparse input. See "
                "TruncatedSVD for a possible alternative."
            )

        if sklearn_check_version('0.23'):
            X = self._validate_data(X, dtype=[np.float64, np.float32],
                                    ensure_2d=True, copy=False)
        else:
            X = _check_array(X, dtype=[np.float64, np.float32],
                             ensure_2d=True, copy=False)

        n_samples, n_features = X.shape
        n_sf_min = min(n_samples, n_features)

        if self.n_components is None:
            if self.svd_solver == "arpack":
                n_components = n_sf_min - 1
            else:
                n_components = n_sf_min
        else:
            n_components = self.n_components

        self._validate_n_components(n_components, n_samples, n_features,
                                    n_sf_min)

        self._fit_svd_solver = self.svd_solver
        shape_good_for_daal = X.shape[1] / X.shape[0] < 2
        if self._fit_svd_solver == "auto":
            if sklearn_check_version('1.1'):
                if max(X.shape) <= 500 or n_components == "mle":
                    self._fit_svd_solver = "full"
                elif 1 <= n_components < 0.8 * n_sf_min:
                    self._fit_svd_solver = "randomized"
                else:
                    self._fit_svd_solver = "full"
            else:
                if n_components == 'mle':
                    self._fit_svd_solver = 'full'
                else:
                    n, p, k = X.shape[0], X.shape[1], n_components
                    # check if sklearnex is faster than randomized sklearn
                    # Refer to daal4py
                    regression_coefs = np.array([
                        [9.779873e-11, n * p * k],
                        [-1.122062e-11, n * p * p],
                        [1.127905e-09, n ** 2],
                    ])

                    if n_components >= 1 and np.dot(
                            regression_coefs[:, 0],
                            regression_coefs[:, 1]) <= 0:
                        self._fit_svd_solver = 'randomized'
                    else:
                        self._fit_svd_solver = 'full'

        if not shape_good_for_daal or self._fit_svd_solver != 'full':
            if sklearn_check_version('0.23'):
                X = self._validate_data(X, copy=self.copy)
            else:
                X = check_array(X, copy=self.copy)

        # Call different fits for either full or truncated SVD
        if shape_good_for_daal and self._fit_svd_solver == "full":
            return dispatch(self, 'fit', {
                'onedal': self.__class__._onedal_fit,
                'sklearn': sklearn_PCA._fit_full,
            }, X)
        elif not shape_good_for_daal and self._fit_svd_solver == "full":
            return sklearn_PCA._fit_full(self, X, n_components)
        elif self._fit_svd_solver in ["arpack", "randomized"]:
            return sklearn_PCA._fit_truncated(
                self, X, n_components, self._fit_svd_solver,
            )
        else:
            raise ValueError(
                "Unrecognized svd_solver='{0}'".format(self._fit_svd_solver)
            )

    def _onedal_gpu_supported(self, method_name, *data):
        if method_name == 'fit':
            return self._fit_svd_solver == 'full'
        elif method_name == 'transform':
            return hasattr(self, '_onedal_estimator')
        raise RuntimeError(
            f'Unknown method {method_name} in {self.__class__.__name__}'
        )

    def _onedal_cpu_supported(self, method_name, *data):
        if method_name == 'fit':
            return self._fit_svd_solver == 'full'
        elif method_name == 'transform':
            return hasattr(self, '_onedal_estimator')
        raise RuntimeError(
            f'Unknown method {method_name} in {self.__class__.__name__}'
        )

    def _onedal_fit(self, X, y=None, queue=None):

        if self.n_components == 'mle' or self.n_components is None:
            onedal_n_components = min(X.shape)
        elif 0 < self.n_components < 1:
            onedal_n_components = min(X.shape)
        else:
            onedal_n_components = self.n_components

        onedal_params = {
            'n_components': onedal_n_components,
            'is_deterministic': True,
            'method': "precomputed",
        }
        self._onedal_estimator = onedal_PCA(**onedal_params)
        self._onedal_estimator.fit(X, queue=queue)
        self._save_attributes()

        U = None
        S = self.singular_values_
        V = self.components_

        return U, S, V

    def _onedal_predict(self, X, queue=None):
        return self._onedal_estimator.predict(X, queue)

    def _onedal_transform(self, X):
        X = _check_array(
            X,
            dtype=[np.float64, np.float32],
            ensure_2d=True,
            copy=False
        )

        if hasattr(self, "n_features_in_"):
            if self.n_features_in_ != X.shape[1]:
                raise ValueError(
                    f"X has {X.shape[1]} features, "
                    f"but {self.__class__.__name__} is expecting "
                    f"{self.n_features_in_} features as input"
                )
        elif hasattr(self, "n_features_"):
            if self.n_features_ != X.shape[1]:
                raise ValueError(
                    f"X has {X.shape[1]} features, "
                    f"but {self.__class__.__name__} is expecting "
                    f"{self.n_features_} features as input"
                )

        # Mean center
        X_centered = X - self.mean_
        return dispatch(self, 'transform', {
            'onedal': self.__class__._onedal_predict,
            'sklearn': sklearn_PCA.transform,
        }, X_centered)

    def transform(self, X):
        check_is_fitted(self)
        if hasattr(self, "_onedal_estimator"):
            X_new = self._onedal_transform(X)[:, : self.n_components_]
            if self.whiten:
                X_new /= np.sqrt(self.explained_variance_)
        else:
            return sklearn_PCA.transform(self, X)
        return X_new

    def fit_transform(self, X, y=None):
        """Fit the model with X and apply the dimensionality reduction on X.
        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            Training data, where `n_samples` is the number of samples
            and `n_features` is the number of features.
        y : Ignored.

        Returns
        -------
        X_new : ndarray of shape (n_samples, n_components)
            Transformed values of X.
        """
        if self.svd_solver in ["randomized", "arpack"]:
            return sklearn_PCA.fit_transform(self, X)
        else:
            self.fit(X)
            if hasattr(self, "_onedal_estimator"):
                X_new = self._onedal_transform(X)[:, : self.n_components_]
                if self.whiten:
                    X_new /= np.sqrt(self.explained_variance_)
                return X_new
            else:
                return sklearn_PCA.transform(self, X)

    def _save_attributes(self):
        self.n_samples_ = self._onedal_estimator.n_samples_

        if sklearn_check_version("1.2"):
            self.n_features_in_ = self._onedal_estimator.n_features_in_
            n_features = self.n_features_in_
        elif sklearn_check_version("0.24"):
            self.n_features_ = self._onedal_estimator.n_features_
            self.n_features_in_ = self._onedal_estimator.n_features_in_
            n_features = self.n_features_in_
        else:
            self.n_features_ = self._onedal_estimator.n_features_
            n_features = self.n_features_
        n_sf_min = min(self.n_samples_, n_features)

        self.mean_ = self._onedal_estimator.mean_
        self.singular_values_ = self._onedal_estimator.singular_values_
        self.explained_variance_ = self._onedal_estimator.explained_variance_
        self.explained_variance_ratio_ = \
            self._onedal_estimator.explained_variance_ratio_

        if self.n_components is None:
            self.n_components_ = self._onedal_estimator.n_components_
        elif self.n_components == 'mle':
            if sklearn_check_version('0.23'):
                self.n_components_ = _infer_dimension(
                    self.explained_variance_, self.n_samples_
                )
            else:
                self.n_components_ = _infer_dimension_(
                    self.explained_variance_, self.n_samples_, n_features
                )
        elif 0 < self.n_components < 1.0:
            ratio_cumsum = stable_cumsum(self.explained_variance_ratio_)
            self.n_components_ = np.searchsorted(
                ratio_cumsum, self.n_components, side='right') + 1
        else:
            self.n_components_ = self._onedal_estimator.n_components_

        if self.n_components_ < n_sf_min:
            if self.explained_variance_.shape[0] == n_sf_min:
                self.noise_variance_ = \
                    self.explained_variance_[self.n_components_:].mean()
            else:
                self.noise_variance_ = self._onedal_estimator.noise_variance_
        else:
            self.noise_variance_ = 0.

        self.explained_variance_ = self.explained_variance_[:self.n_components_]
        self.explained_variance_ratio_ = \
            self.explained_variance_ratio_[:self.n_components_]
        self.components_ = \
            self._onedal_estimator.components_[:self.n_components_]
        self.singular_values_ = self.singular_values_[:self.n_components_]
