text: # coding=utf-8
  # Copyright The XTREME Benchmark Authors.
  #
  # 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.
  """Evaluation."""
  
  import argparse
  from seqeval.metrics import precision_score, recall_score, f1_score
  import sys
  
  from third_party.evaluate_squad import evaluate as squad_eval
  from third_party.evaluate_mlqa import evaluate as mlqa_eval
  
  def read_tag(file):
    labels = []
    example = []
    for line in open(file, 'r'):
      items = line.strip().split('\t')
      if len(items) == 2:
        example.append(items[1].strip())
      else:
        labels.append(example)
        example = []
    return labels
  
  
  def read_label(file):
    return [l.strip() for l in open(file)]
  
  
  def read_squad(file):
    expected_version = '1.1'
    with open(file) as dataset_file:
      dataset_json = json.load(dataset_file)
      if 'version' in dataset_json and dataset_json['version'] != expected_version:
        print('Evaluation expects v-' + expected_version,
              ', but got dataset with v-' + dataset_json['version'],
              file=sys.stderr)
      if 'data' in dataset_json:
        return dataset_json['data']
      else:
        return dataset_json
  
  
  def f1(labels, predictions, language=None):
    f1 = f1_score(labels, predictions)
    precision = precision_score(labels, predictions)
    recall = recall_score(labels, predictions)
    return {'f1': f1, 'precision': precision, 'recall': recall}
  
  
  def accuracy(labels, predictions, language=None):
    correct = sum([int(p == l) for p, l in zip(predictions, labels)])
    accuracy = float(correct) / len(predictions)
    return {'accuracy': accuracy}
  
  
  def squad_em_f1(labels, predictions, language=None):
    return squad_eval(labels, predictions)
  
  def mlqa_em_f1(labels, predictions, language):
    if language is None:
      print('required 2-char language code for the argument `language`')
      exit(0)
    return mlqa_eval(labels, predictions)
  
  
  GROUP2TASK = {
    "classification": ["pawsx", "xnli"],
    "tagging": ["udpos", "panx"],
    "qa": ["xquad", "mlqa", "tydiqa"],
    "retrieval": ["bucc2018", "tatoeba"],
  }
  
  
  TASK2LANGS = {
    "pawsx": "de,en,es,fr,ja,ko,zh".split(","),
    "xnli": "ar,bg,de,el,en,es,fr,hi,ru,sw,th,tr,ur,vi,zh".split(","),
    "panx": "ar,he,vi,id,jv,ms,tl,eu,ml,ta,te,af,nl,en,de,el,bn,hi,mr,ur,fa,fr,it,pt,es,bg,ru,ja,ka,ko,th,sw,yo,my,zh,kk,tr,et,fi,hu".split(","),
    "udpos": "af,ar,bg,de,el,en,es,et,eu,fa,fi,fr,he,hi,hu,id,it,ja,kk,ko,mr,nl,pt,ru,ta,te,th,tl,tr,ur,vi,yo,zh".split(","),
    "bucc2018": "de,fr,ru,zh".split(","),
    "tatoeba": "ar,he,vi,id,jv,tl,eu,ml,ta,te,af,nl,en,de,el,bn,hi,mr,ur,fa,fr,it,pt,es,bg,ru,ja,ka,ko,th,sw,zh,kk,tr,et,fi,hu".split(","),
    "xquad": "en,es,de,el,ru,tr,ar,vi,th,zh,hi".split(","),
    "mlqa": "en,es,de,ar,hi,vi,zh".split(","),
    "tydiqa": "en,ar,bn,fi,id,ko,ru,sw,te".split(","),
  }
  
  
  READER_FUNCTION = {
    'pawsx': read_label,
    'xnli': read_label,
    'panx': read_tag,
    'udpos': read_tag,
    'bucc2018': read_label,
    'tatoeba': read_label,
    'xquad': read_squad,
    'mlqa': read_squad,
    'tydiqa': read_squad,
  }
  
  
  METRIC_FUNCTION = {
    'pawsx': accuracy,
    'xnli': accuracy,
    'panx': f1,
    'udpos': f1,
    'bucc2018': f1,
    'tatoeba': accuracy,
    'xquad': squad_em_f1,
    'mlqa': mlqa_em_f1,
    'tydiqa': squad_em_f1,
  }
  
  
  def evaluate_one_task(prediction_file, label_file, task, language=None):
    """Evalute the classification tasks by accuracy.
    Args:
      prediction_file (string): path to the prediction tsv file. 
      label_file (string): path to the grouth truth tsv file.
    Return: 
      result (dict): a dictionary with accuracy.
  
    Both input files contain one example per line as follows:
      ``[label]\t[sentence1]\t[sentence2]``
    """
    predictions = READER_FUNCTION[task](prediction_file)
    labels = READER_FUNCTION[task](label_file)
    assert len(predictions) == len(labels), 'Number of examples in {} and {} not matched'.format(prediction_file, label_file)
    result = METRIC_FUNCTION[task](labels, predictions)
    return result
  
  
  def evaluate(prediction_folder, label_folder):
    """Evaluate on all tasks if available.
    Args:
      prediction_folder (string): prediction folder that contains each task's prediction in each subfolder.
      label_file (string): label folder that contains each task's ground-truth label in each subfolder.
    Return:
      overall_scores (dict): a dictionary with sub-group scores. key: group label.
      detailed_scores (dict): a dictionary with all detailed scores. key: task label.
    """
    prediction_tasks = next(os.walk(prediction_folder))[1]
    label_tasks = next(os.walk(label_folder))[1]
    
    detailed_scores = {}
    for task, langs in TASK2LANGS.items():
      if task in prediction_tasks and task in label_tasks:
        suffix = "json" if task in GROUP2TASK["qa"] else "tsv"
        # collect scores over all languages
        score = defaultdict(dict)
        for lg in langs:
          prediction_file = os.path.join(prediction_folder, task, f"test-{lg}.{suffix}")
          label_file = os.path.join(label_folder, task, f"test-{lg}.{suffix}")
          score_lg = evaluate_one_task(prediction_file, label_file, task, language=lg)
          for metric in score_lg.items():
            score[metric][lg] = score_lg[metric]
        # average over all languages
        for m in score: 
          score[f'avg_{m}'] = sum(score[m].values()) / len(score[m])
        if task in GROUP2TASK["qa"]:
          score['avg_metric'] = (score['avg_exact_match'] + score['avg_f1']) / 2
        elif 'avg_f1' in score:
          score['avg_metric'] = score['avg_f1']
        elif 'avg_accuracy' in score:
          score['avg_metric'] = score['avg_accuracy']
        detailed_scores[task] = score
  
    # Display logic: 
    # If scores of all tasks in a sub group are available, show the score in the sub table
    overall_scores = {}
    for group in GROUP2TASK:
      if all(task in detailed_scores for task in group):
        overall_scores[group] = sum(detailed_scores[task]['avg_metric'] for task in group) / len(group)
    
    # If scores of all tasks are available, show the overall score in the main table
    all_tasks = list(TASK2LANGS.keys())
    if all(task in all_tasks for task in detailed_scores):
      overall_scores['all_task'] = sum(detailed_scores[task]['avg_metric'] for task in all_tasks) / len(all_tasks)
  
    return overall_scores, detailed_scores
  
  
  if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--prediction_folder', default=None, type=str, required=True,
      help='the predictions of one model')
    parser.add_argument('--label_folder', default=None, type=str, required=True,
      help='the grouth truth file')
    args = parser.parse_args()
    overall_scores, detailed_scores = evaluate(args.prediction_folder, args.label_folder)
    print(overall_scores)
  