run_docvqa.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
""" Fine-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """

from __future__ import absolute_import, division, print_function

import argparse
import glob
import logging
import os
import shutil
import random
import json 
import timeit

import numpy as np
import torch

from tensorboardX import SummaryWriter
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from utils_docvqa import (
    read_docvqa_examples,
    convert_examples_to_features)
import sys
from modeling_layoutlm import LayoutLMForTokenClassification
from transformers import AdamW, get_linear_schedule_with_warmup
from transformers import (
    WEIGHTS_NAME,
    BertConfig,
    BertForTokenClassification,
    BertTokenizer,
)
from transformers import RobertaConfig, RobertaForTokenClassification, RobertaTokenizer
from transformers import (
    DistilBertConfig,
    DistilBertForTokenClassification,
    DistilBertTokenizer,
)
from transformers.data.metrics.squad_metrics import (
    compute_predictions_log_probs,
    compute_predictions_logits,
    squad_evaluate,
)
from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor

logger = logging.getLogger(__name__)



MODEL_CLASSES = {
    "layoutlm": (BertConfig, LayoutLMForTokenClassification, BertTokenizer),
}


def set_seed(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)


def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id):
    """ Train the model """
    if args.local_rank in [-1, 0]:
        tb_writer = SummaryWriter()

    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
    train_sampler = (
        RandomSampler(train_dataset)
        if args.local_rank == -1
        else DistributedSampler(train_dataset)
    )
    train_dataloader = DataLoader(
        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size
    )

    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = (
            args.max_steps
            // (len(train_dataloader) // args.gradient_accumulation_steps)
            + 1
        )
    else:
        t_total = (
            len(train_dataloader)
            // args.gradient_accumulation_steps
            * args.num_train_epochs
        )

    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [
                p
                for n, p in model.named_parameters()
                if not any(nd in n for nd in no_decay)
            ],
            "weight_decay": args.weight_decay,
        },
        {
            "params": [
                p
                for n, p in model.named_parameters()
                if any(nd in n for nd in no_decay)
            ],
            "weight_decay": 0.0,
        },
    ]
    optimizer = AdamW(
        optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon
    )
    scheduler = get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
    )
    if args.fp16:
        try:
            from apex import amp
        except ImportError:
            raise ImportError(
                "Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
            )
        model, optimizer = amp.initialize(
            model, optimizer, opt_level=args.fp16_opt_level
        )

    # multi-gpu training (should be after apex fp16 initialization)
    if args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    # Distributed training (should be after apex fp16 initialization)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(
            model,
            device_ids=[args.local_rank],
            output_device=args.local_rank,
            find_unused_parameters=True,
        )

    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(train_dataset))
    logger.info("  Num Epochs = %d", args.num_train_epochs)
    logger.info(
        "  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size
    )
    logger.info(
        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
        args.train_batch_size
        * args.gradient_accumulation_steps
        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
    )
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

    global_step = 0
    tr_loss, logging_loss = 0.0, 0.0
    model.zero_grad()
    train_iterator = trange(
        int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
    )
    set_seed(args)  # Added here for reproductibility (even between python 2 and 3)
    for _ in train_iterator:
        epoch_iterator = tqdm(
            train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0]
        )
        for step, batch in enumerate(epoch_iterator):
            model.train()
            if args.model_type != "layoutlm":
                batch = batch[:4]
            batch = tuple(t.to(args.device) for t in batch)
            inputs = {
                "input_ids": batch[0],
                "attention_mask": batch[1],
                "start_positions": batch[3],
                "end_positions":batch[4],
            }

            if args.model_type == "layoutlm":
                inputs["bbox"] = batch[5]
            if args.model_type != "distilbert":
                inputs["token_type_ids"] = (
                    batch[2]
                    if args.model_type in ["bert", "xlnet", "layoutlm"]
                    else None
                )  # XLM and RoBERTa don"t use segment_ids
            outputs = model(**inputs)
            loss = outputs[0]
             # model outputs are always tuple in pytorch-transformers (see doc)

            if args.n_gpu > 1:
                loss = loss.mean()  # mean() to average on multi-gpu parallel training
            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps

            if args.fp16:
                with amp.scale_loss(loss, optimizer) as scaled_loss:
                    scaled_loss.backward()
            else:
                loss.backward()

            tr_loss += loss.item()
            if (step + 1) % args.gradient_accumulation_steps == 0:
                if args.fp16:
                    torch.nn.utils.clip_grad_norm_(
                        amp.master_params(optimizer), args.max_grad_norm
                    )
                else:
                    torch.nn.utils.clip_grad_norm_(
                        model.parameters(), args.max_grad_norm
                    )

                scheduler.step()  # Update learning rate schedule
                optimizer.step()
                model.zero_grad()
                global_step += 1

                if (
                    args.local_rank in [-1, 0]
                    and args.logging_steps > 0
                    and global_step % args.logging_steps == 0
                ):
                    # Log metrics
                    if (
                        args.local_rank == -1 and args.evaluate_during_training
                    ):  # Only evaluate when single GPU otherwise metrics may not average well
                        results = evaluate(
                            args,
                            model,
                            tokenizer,
                            labels,
                            pad_token_label_id,
                            mode="dev",
                        )
                        for key, value in results.items():
                            tb_writer.add_scalar(
                                "eval_{}".format(key), value, global_step
                            )
                    tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
                    tb_writer.add_scalar(
                        "loss",
                        (tr_loss - logging_loss) / args.logging_steps,
                        global_step,
                    )
                    logging_loss = tr_loss

                if (
                    args.local_rank in [-1, 0]
                    and args.save_steps > 0
                    and global_step % args.save_steps == 0
                ):
                    # Save model checkpoint
                    output_dir = os.path.join(
                        args.output_dir, "checkpoint-{}".format(global_step)
                    )
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    model_to_save = (
                        model.module if hasattr(model, "module") else model
                    )  # Take care of distributed/parallel training
                    model_to_save.save_pretrained(output_dir)
                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
                    logger.info("Saving model checkpoint to %s", output_dir)

            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
        if args.max_steps > 0 and global_step > args.max_steps:
            train_iterator.close()
            break

    if args.local_rank in [-1, 0]:
        tb_writer.close()

    return global_step, tr_loss / global_step

def to_list(tensor):
    return tensor.detach().cpu().tolist()
def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""):
    eval_dataset,features, examples = load_and_cache_examples(
        args, tokenizer, labels, pad_token_label_id, mode=mode
    )

    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
    # Note that DistributedSampler samples randomly
    eval_sampler = (
        SequentialSampler(eval_dataset)
        if args.local_rank == -1
        else DistributedSampler(eval_dataset)
    )
    eval_dataloader = DataLoader(
        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size
    )

    # Eval!
    logger.info("***** Running evaluation %s *****", prefix)
    logger.info("  Num examples = %d", len(eval_dataset))
    logger.info("  Batch size = %d", args.eval_batch_size)
    eval_loss = 0.0
    nb_eval_steps = 0
    preds = None
    out_label_ids = None
    all_results = []
    start_time = timeit.default_timer()
    model.eval()
    for batch in tqdm(eval_dataloader, desc="Evaluating"):
        batch = tuple(t.to(args.device) for t in batch)
        with torch.no_grad():
                inputs = {
                    "input_ids": batch[0],
                    "attention_mask": batch[1],
                }
                inputs["bbox"] = batch[5]
                inputs["token_type_ids"] = (batch[6])
                outputs = model(**inputs)
                example_indices = batch[7]
        for i, example_index in enumerate(example_indices):
            eval_feature = features[example_index.item()]
            unique_id = int(eval_feature.unique_id)

            output = [to_list(output[i]) for output in outputs]

            start_logits, end_logits = output
            result = SquadResult(unique_id, start_logits, end_logits)
            all_results.append(result)
    evalTime = timeit.default_timer() - start_time
    logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
    output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
    predictions = compute_predictions_logits(
        examples,
        features,
        all_results,
        20, 
        30, 
        args.do_lower_case,
        output_prediction_file,
        output_nbest_file,
        output_null_log_odds_file,
        True,
        True,
        0.0,
        tokenizer,
    )

    # Compute the F1 and exact scores.
    results = squad_evaluate(examples, predictions)
    return results

def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode):
    if args.local_rank not in [-1, 0] and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Load data features from cache or dataset file
    cached_features_file = os.path.join(
        args.data_dir,
        "cached_{}_{}_{}".format(
            mode,
            list(filter(None, args.model_name_or_path.split("/"))).pop(),
            str(args.max_seq_length),
        ),
    )
    if mode == 'train':
        train_examples = read_docvqa_examples(args.train_json,is_training=True,skip_match_answers=args.skip_match_answers)
        logger.info("Loading train json from %s", args.train_json)
    else:
        train_examples = read_docvqa_examples(args.val_json,is_training=True,skip_match_answers=args.skip_match_answers)
        logger.info("Loading val json from %s", args.val_json)
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        if mode == 'train':
            train_examples = read_docvqa_examples(args.train_json,is_training=True,skip_match_answers=args.skip_match_answers)

        else:
            train_examples = read_docvqa_examples(args.val_json,is_training=True,skip_match_answers=args.skip_match_answers)
        max_query_length = 64
        doc_stride = args.doc_stride
        features = convert_examples_to_features(
            examples=train_examples,
            label_list=labels,
            tokenizer=tokenizer,
            max_seq_length=args.max_seq_length,
            doc_stride=doc_stride,
            max_query_length=max_query_length,
            is_training=True,
            pad_token_label_id=pad_token_label_id)    
        print("Features generated",mode)
        if args.local_rank in [-1, 0]:
            logger.info("Saving features into cached file %s", cached_features_file)
            torch.save(features, cached_features_file)
    
    if args.local_rank == 0 and not evaluate:
        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
    all_bboxes = torch.tensor([f.boxes for f in features], dtype=torch.long)
    all_start_positions = torch.tensor([f.start_positions for f in features], dtype=torch.long) 
    all_end_positions = torch.tensor([f.end_positions for f in features], dtype=torch.long)
    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.long)
    all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
    dataset = TensorDataset(
        all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions, all_bboxes, all_p_mask,all_example_index
    )
    return dataset, features, train_examples


def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument(
        "--data_dir",
        default=None,
        type=str,
        required=True,
        help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.",
    )
    parser.add_argument(
        "--model_type",
        default=None,
        type=str,
        required=True,
        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
    )
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        required=True,
        help="Path to pre-trained model ",
    )
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )

    ## Other parameters
    parser.add_argument(
        "--config_name",
        default="",
        type=str,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        default="",
        type=str,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--cache_dir",
        default="",
        type=str,
        help="Where do you want to store the pre-trained models downloaded from s3",
    )
    parser.add_argument(
        "--max_seq_length",
        default=128,
        type=int,
        help="The maximum total input sequence length after tokenization. Sequences longer "
        "than this will be truncated, sequences shorter will be padded.",
    )
    parser.add_argument(
        "--doc_stride",
        default=128,
        type=int,
        help="Stride for documents with tokens more than max_seq_len",
    )
    parser.add_argument(
        "--do_train", action="store_true", help="Whether to run training."
    )
    parser.add_argument(
        "--do_eval", action="store_true", help="Whether to run eval on the dev set."
    )
    parser.add_argument(
        "--do_predict",
        action="store_true",
        help="Whether to run predictions on the test set.",
    )
    parser.add_argument(
        "--evaluate_during_training",
        action="store_true",
        help="Whether to run evaluation during training at each logging step.",
    )
    parser.add_argument(
        "--do_lower_case",
        action="store_true",
        help="Set this flag if you are using an uncased model.",
    )

    parser.add_argument(
        "--per_gpu_train_batch_size",
        default=8,
        type=int,
        help="Batch size per GPU/CPU for training.",
    )
    parser.add_argument(
        "--per_gpu_eval_batch_size",
        default=8,
        type=int,
        help="Batch size per GPU/CPU for evaluation.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--learning_rate",
        default=5e-5,
        type=float,
        help="The initial learning rate for Adam.",
    )
    parser.add_argument(
        "--weight_decay", default=0.0, type=float, help="Weight decay if we apply some."
    )
    parser.add_argument(
        "--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer."
    )
    parser.add_argument(
        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm."
    )
    parser.add_argument(
        "--num_train_epochs",
        default=3.0,
        type=float,
        help="Total number of training epochs to perform.",
    )
    parser.add_argument(
        "--max_steps",
        default=-1,
        type=int,
        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
    )
    parser.add_argument(
        "--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps."
    )

    parser.add_argument(
        "--logging_steps", type=int, default=50, help="Log every X updates steps."
    )
    parser.add_argument(
        "--save_steps",
        type=int,
        default=50,
        help="Save checkpoint every X updates steps.",
    )
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
    )
    parser.add_argument(
        "--no_cuda", action="store_true", help="Avoid using CUDA when available"
    )
    parser.add_argument(
        "--overwrite_output_dir",
        action="store_true",
        help="Overwrite the content of the output directory",
    )
    parser.add_argument(
        "--overwrite_cache",
        action="store_true",
        help="Overwrite the cached training and evaluation sets",
    )
    parser.add_argument(
        "--seed", type=int, default=42, help="random seed for initialization"
    )

    parser.add_argument(
        "--fp16",
        action="store_true",
        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
    )
    parser.add_argument(
        "--fp16_opt_level",
        type=str,
        default="O1",
        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
    parser.add_argument(
        "--local_rank",
        type=int,
        default=-1,
        help="For distributed training: local_rank",
    )
    parser.add_argument(
        "--server_ip", type=str, default="", help="For distant debugging."
    )
    parser.add_argument(
        "--server_port", type=str, default="", help="For distant debugging."
    )
    parser.add_argument(
        "--train_json", type=str
    )
    parser.add_argument(
        "--val_json", type=str
    )
    parser.add_argument(
        "--skip_match_answers",
        action="store_true",
        help="Whether to match OCR start and end index to groundtruth answers",
    )
    args = parser.parse_args()

    if (
        os.path.exists(args.output_dir)
        and os.listdir(args.output_dir)
        and args.do_train
        and not args.overwrite_output_dir
    ):
        raise ValueError(
            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
                args.output_dir
            )
        )

    # Setup distant debugging if needed
    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("Waiting for debugger attach")
        ptvsd.enable_attach(
            address=(args.server_ip, args.server_port), redirect_output=True
        )
        ptvsd.wait_for_attach()

    # Setup CUDA, GPU & distributed training
    if args.local_rank == -1 or args.no_cuda:
        device = torch.device(
            "cuda:0" if torch.cuda.is_available() and not args.no_cuda else "cpu"
        )
        torch.cuda.set_device(device)
        args.n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        torch.distributed.init_process_group(backend="nccl")
        args.n_gpu = 1
    args.device = device

    if args.overwrite_output_dir and os.path.exists(args.output_dir):
        shutil.rmtree(args.output_dir)
    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)
    # Setup logging
    logging.basicConfig(
        filename=os.path.join(args.output_dir, "train.log"),
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        args.local_rank,
        device,
        args.n_gpu,
        bool(args.local_rank != -1),
        args.fp16,
    )

    # Set seed
    set_seed(args)

    # Prepare CONLL-2003 task
    labels = ["start","end"]
    num_labels = len(labels)
    # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
    pad_token_label_id = CrossEntropyLoss().ignore_index

    # Load pretrained model and tokenizer
    if args.local_rank not in [-1, 0]:
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

    args.model_type = args.model_type.lower()
    print("ARGS",args)
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    print("Config_name",args.config_name)
    
    config = config_class.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        num_labels=num_labels,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
    tokenizer = tokenizer_class.from_pretrained(
        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
        do_lower_case=args.do_lower_case,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
    model = model_class.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )

    if args.local_rank == 0:
        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab

    model.to(args.device)

    logger.info("Training/evaluation parameters %s", args)

    # Training
    if args.do_train:
        print("tokenizer",tokenizer)
        print("pad_token_label_id",pad_token_label_id)
        print("labels",labels) 
        train_dataset,_,_ = load_and_cache_examples(
            args, tokenizer, labels, pad_token_label_id, mode="train"
        )
        global_step, tr_loss = train(
            args, train_dataset, model, tokenizer, labels, pad_token_label_id
        )
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        # Create output directory if needed
        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
            os.makedirs(args.output_dir)

        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        model_to_save = (
            model.module if hasattr(model, "module") else model
        )  # Take care of distributed/parallel training
        model_to_save.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))

    # Evaluation
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        tokenizer = tokenizer_class.from_pretrained(
            'data_docvqa_train_test', do_lower_case=args.do_lower_case
        )
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(
                os.path.dirname(c)
                for c in sorted(
                    glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)
                )
            )
            logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
                logging.WARN
            )  # Reduce logging
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
            model = model_class.from_pretrained(checkpoint)
            model.to(args.device)
            result = evaluate(
                args,
                model,
                tokenizer,
                labels,
                pad_token_label_id,
                mode="dev",
                prefix=global_step,
            )
            if global_step:
                result = {"{}_{}".format(global_step, k): v for k, v in result.items()}
            results.update(result)
        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
        with open(output_eval_file, "w") as writer:
            for key in sorted(results.keys()):
                writer.write("{} = {}\n".format(key, str(results[key])))

    if args.do_predict and args.local_rank in [-1, 0]:
        tokenizer = tokenizer_class.from_pretrained(
            args.output_dir, do_lower_case=args.do_lower_case
        )
        model = model_class.from_pretrained(args.output_dir)
        model.to(args.device)
        result, predictions = evaluate(
            args, model, tokenizer, labels, pad_token_label_id, mode="test"
        )
        with open('./tmp.json','w') as fp:
            json.dump(predictions,fp)
        # Save results
        output_test_results_file = os.path.join(args.output_dir, "test_results.txt")
        with open(output_test_results_file, "w") as writer:
            for key in sorted(result.keys()):
                writer.write("{} = {}\n".format(key, str(result[key])))
        # Save predictions
        output_test_predictions_file = os.path.join(
            args.output_dir, "test_predictions.txt"
        )
        with open(output_test_predictions_file, "w") as writer:
            with open(os.path.join(args.data_dir, "test.txt"), "r") as f:
                example_id = 0
                for line in f:
                    if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                        writer.write(line)
                        if not predictions[example_id]:
                            example_id += 1
                    elif predictions[example_id]:
                        output_line = (
                            line.split()[0]
                            + " "
                            + predictions[example_id].pop(0)
                            + "\n"
                        )
                        writer.write(output_line)
                    else:
                        logger.warning(
                            "Maximum sequence length exceeded: No prediction for '%s'.",
                            line.split()[0],
                        )

    return results


if __name__ == "__main__":
    main()