text: #  Tencent is pleased to support the open source community by making GNES available.
  #
  #  Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
  #  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.
  
  # pylint: disable=low-comment-ratio
  
  
  import numpy as np
  
  from ..base import BaseNumericEncoder
  from ...helper import get_perm, batching, get_optimal_sample_size, train_required
  
  
  class PCALocalEncoder(BaseNumericEncoder):
      def __init__(self, output_dim: int, num_locals: int,
                   *args, **kwargs):
          super().__init__(*args, **kwargs)
          assert output_dim >= num_locals and output_dim % num_locals == 0, \
              'output_dim should >= num_locals and can be divided by num_locals!'
          self.output_dim = output_dim
          self.num_locals = num_locals
          self.pca_components = None
          self.mean = None
          self.batch_size = 2048
  
      @batching(batch_size=get_optimal_sample_size, num_batch=1)
      def train(self, vecs: np.ndarray, *args, **kwargs) -> None:
          import faiss
          num_samples, num_dim = vecs.shape
          assert self.output_dim <= num_samples, 'training PCA requires at least %d points, but %d was given' % (
              self.output_dim, num_samples)
          assert self.output_dim < num_dim, 'PCA output dimension should < data dimension, received (%d, %d)' % (
              self.output_dim, num_dim)
  
          pca = faiss.PCAMatrix(num_dim, self.output_dim)
          self.mean = np.mean(vecs, axis=0)  # 1 x 768
          pca.train(vecs)
          explained_variance_ratio = faiss.vector_to_array(pca.eigenvalues)[:self.output_dim]
          components = faiss.vector_to_array(pca.PCAMat).reshape([-1, num_dim])[:self.output_dim]
  
          # permutate engive according to variance
          opt_order = get_perm(explained_variance_ratio, self.num_locals)
          comp_tmp = np.reshape(components[opt_order], [self.output_dim, num_dim])
  
          self.pca_components = np.transpose(comp_tmp)  # 768 x 200
  
      @train_required
      @batching
      def encode(self, vecs: np.ndarray, *args, **kwargs) -> np.ndarray:
          return np.matmul(vecs - self.mean, self.pca_components)
  
      def _copy_from(self, x: 'PCALocalEncoder') -> None:
          self.output_dim = x.output_dim
          self.pca_components = x.pca_components
          self.mean = x.mean
          self.num_locals = x.num_locals
          self.is_trained = x.is_trained
  