def batch_evaluate(self, pred_json, true_json):
        ''' Computes TEDS score between the prediction and the ground truth of
            a batch of samples
            @params pred_json: {'FILENAME': 'HTML CODE', ...}
            @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...}
            @output: {'FILENAME': 'TEDS SCORE', ...}
        '''
        samples = true_json.keys()
        # if self.n_jobs == 1:
        scores = [self.evaluate(pred_json.get(filename, ''), true_json[filename]['html']) for filename in tqdm(samples)]
        # else:
        #     inputs = [{'pred': pred_json.get(filename, ''), 'true': true_json[filename]['html']} for filename in samples]
        #     scores = parallel_process(inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1)
        scores = dict(zip(samples, scores))
        return scores
 Computes TEDS score between the prediction and the ground truth of
            a batch of samples
            @params pred_json: {'FILENAME': 'HTML CODE', ...}
            @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...}
            @output: {'FILENAME': 'TEDS SCORE', ...}
        
vlmeval/dataset/Omnidocbench/metrics.py