text: from . import SentenceEvaluator, SimilarityFunction
  from torch.utils.data import DataLoader
  
  import torch
  import logging
  from tqdm import tqdm
  from ..util import batch_to_device
  import os
  import csv
  from sklearn.metrics.pairwise import paired_cosine_distances, paired_euclidean_distances, paired_manhattan_distances
  from scipy.stats import pearsonr, spearmanr
  import numpy as np
  
  class EmbeddingSimilarityEvaluator(SentenceEvaluator):
      """
      Evaluate a model based on the similarity of the embeddings by calculating the Spearman and Pearson rank correlation
      in comparison to the gold standard labels.
      The metrics are the cosine similarity as well as euclidean and Manhattan distance
      The returned score is the Spearman correlation with a specified metric.
  
      The results are written in a CSV. If a CSV already exists, then values are appended.
      """
  
  
      def __init__(self, dataloader: DataLoader, main_similarity: SimilarityFunction = None, name: str = '', show_progress_bar: bool = None):
          """
          Constructs an evaluator based for the dataset
  
          The labels need to indicate the similarity between the sentences.
  
          :param dataloader:
              the data for the evaluation
          :param main_similarity:
              the similarity metric that will be used for the returned score
          """
          self.dataloader = dataloader
          self.main_similarity = main_similarity
          self.name = name
          if name:
              name = "_"+name
  
          if show_progress_bar is None:
              show_progress_bar = (logging.getLogger().getEffectiveLevel() == logging.INFO or logging.getLogger().getEffectiveLevel() == logging.DEBUG)
          self.show_progress_bar = show_progress_bar
  
          self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
          self.csv_file = "similarity_evaluation"+name+"_results.csv"
          self.csv_headers = ["epoch", "steps", "cosine_pearson", "cosine_spearman", "euclidean_pearson", "euclidean_spearman", "manhattan_pearson", "manhattan_spearman", "dot_pearson", "dot_spearman"]
  
      def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float:
          model.eval()
          embeddings1 = []
          embeddings2 = []
          labels = []
  
          if epoch != -1:
              if steps == -1:
                  out_txt = " after epoch {}:".format(epoch)
              else:
                  out_txt = " in epoch {} after {} steps:".format(epoch, steps)
          else:
              out_txt = ":"
  
          logging.info("Evaluation the model on "+self.name+" dataset"+out_txt)
  
          self.dataloader.collate_fn = model.smart_batching_collate
  
          iterator = self.dataloader
          # if self.show_progress_bar:
          #     iterator = tqdm(iterator, desc="Convert Evaluating")
  
          for step, batch in enumerate(iterator):
              features, label_ids = batch_to_device(batch, self.device)
              with torch.no_grad():
                  emb1, emb2 = [model(sent_features)['sentence_embedding'].to("cpu").numpy() for sent_features in features]
  
              labels.extend(label_ids.to("cpu").numpy())
              embeddings1.extend(emb1)
              embeddings2.extend(emb2)
  
          try:
              cosine_scores = 1 - (paired_cosine_distances(embeddings1, embeddings2))
          except Exception as e:
              print(embeddings1)
              print(embeddings2)
              raise(e)
  
          manhattan_distances = -paired_manhattan_distances(embeddings1, embeddings2)
          euclidean_distances = -paired_euclidean_distances(embeddings1, embeddings2)
          dot_products = [np.dot(emb1, emb2) for emb1, emb2 in zip(embeddings1, embeddings2)]
  
  
          eval_pearson_cosine, _ = pearsonr(labels, cosine_scores)
          eval_spearman_cosine, _ = spearmanr(labels, cosine_scores)
  
          eval_pearson_manhattan, _ = pearsonr(labels, manhattan_distances)
          eval_spearman_manhattan, _ = spearmanr(labels, manhattan_distances)
  
          eval_pearson_euclidean, _ = pearsonr(labels, euclidean_distances)
          eval_spearman_euclidean, _ = spearmanr(labels, euclidean_distances)
  
          eval_pearson_dot, _ = pearsonr(labels, dot_products)
          eval_spearman_dot, _ = spearmanr(labels, dot_products)
  
          logging.info("\n\nCosine-Similarity :\tPearson: {:.4f}\tSpearman: {:.4f}".format(
              eval_pearson_cosine, eval_spearman_cosine))
          logging.info("Manhattan-Distance:\tPearson: {:.4f}\tSpearman: {:.4f}".format(
              eval_pearson_manhattan, eval_spearman_manhattan))
          logging.info("Euclidean-Distance:\tPearson: {:.4f}\tSpearman: {:.4f}".format(
              eval_pearson_euclidean, eval_spearman_euclidean))
          logging.info("Dot-Product-Similarity:\tPearson: {:.4f}\tSpearman: {:.4f}".format(
              eval_pearson_dot, eval_spearman_dot))
  
          if output_path is not None:
              csv_path = os.path.join(output_path, self.csv_file)
              output_file_exists = os.path.isfile(csv_path)
              with open(csv_path, mode="a" if output_file_exists else 'w', encoding="utf-8") as f:
                  writer = csv.writer(f)
                  if not output_file_exists:
                      writer.writerow(self.csv_headers)
  
                  writer.writerow([epoch, steps, eval_pearson_cosine, eval_spearman_cosine, eval_pearson_euclidean,
                                   eval_spearman_euclidean, eval_pearson_manhattan, eval_spearman_manhattan, eval_pearson_dot, eval_spearman_dot])
  
  
          if self.main_similarity == SimilarityFunction.COSINE:
              return eval_spearman_cosine
          elif self.main_similarity == SimilarityFunction.EUCLIDEAN:
              return eval_spearman_euclidean
          elif self.main_similarity == SimilarityFunction.MANHATTAN:
              return eval_spearman_manhattan
          elif self.main_similarity == SimilarityFunction.DOT_PRODUCT:
              return eval_spearman_dot
          elif self.main_similarity is None:
              return max(eval_spearman_cosine, eval_spearman_manhattan, eval_spearman_euclidean, eval_spearman_dot)
          else:
              raise ValueError("Unknown main_similarity value")
  