joseangelatm
/

PanamianModel

@@ -1,783 +0,0 @@
-# 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 language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
-GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
-using a masked language modeling (MLM) loss.
-"""
-import argparse
-import glob
-import logging
-import os
-import pickle
-import random
-import re
-import shutil
-from typing import Dict, List, Tuple
-import numpy as np
-import torch
-from torch.nn.utils.rnn import pad_sequence
-from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-from transformers import (
-    MODEL_WITH_LM_HEAD_MAPPING,
-    WEIGHTS_NAME,
-    AdamW,
-    AutoConfig,
-    AutoModelWithLMHead,
-    AutoTokenizer,
-    PreTrainedModel,
-    PreTrainedTokenizer,
-    get_linear_schedule_with_warmup,
-)
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    from tensorboardX import SummaryWriter
-logger = logging.getLogger(__name__)
-MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-class TextDataset(Dataset):
-    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
-        assert os.path.isfile(file_path)
-        block_size = block_size - (tokenizer.max_len - tokenizer.max_len_single_sentence)
-        directory, filename = os.path.split(file_path)
-        cached_features_file = os.path.join(
-            directory, args.model_type + "_cached_lm_" + str(block_size) + "_" + filename
-        )
-        if os.path.exists(cached_features_file) and not args.overwrite_cache:
-            logger.info("Loading features from cached file %s", cached_features_file)
-            with open(cached_features_file, "rb") as handle:
-                self.examples = pickle.load(handle)
-        else:
-            logger.info("Creating features from dataset file at %s", directory)
-            self.examples = []
-            with open(file_path, encoding="utf-8") as f:
-                text = f.read()
-            tokenized_text = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text))
-            for i in range(0, len(tokenized_text) - block_size + 1, block_size):  # Truncate in block of block_size
-                self.examples.append(tokenizer.build_inputs_with_special_tokens(tokenized_text[i : i + block_size]))
-            # Note that we are loosing the last truncated example here for the sake of simplicity (no padding)
-            # If your dataset is small, first you should loook for a bigger one :-) and second you
-            # can change this behavior by adding (model specific) padding.
-            logger.info("Saving features into cached file %s", cached_features_file)
-            with open(cached_features_file, "wb") as handle:
-                pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
-    def __len__(self):
-        return len(self.examples)
-    def __getitem__(self, item):
-        return torch.tensor(self.examples[item], dtype=torch.long)
-class LineByLineTextDataset(Dataset):
-    def __init__(self, tokenizer: PreTrainedTokenizer, args, file_path: str, block_size=512):
-        assert os.path.isfile(file_path)
-        # Here, we do not cache the features, operating under the assumption
-        # that we will soon use fast multithreaded tokenizers from the
-        # `tokenizers` repo everywhere =)
-        logger.info("Creating features from dataset file at %s", file_path)
-        with open(file_path, encoding="utf-8") as f:
-            lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]
-        self.examples = tokenizer.batch_encode_plus(lines, add_special_tokens=True, max_length=block_size)["input_ids"]
-    def __len__(self):
-        return len(self.examples)
-    def __getitem__(self, i):
-        return torch.tensor(self.examples[i], dtype=torch.long)
-def load_and_cache_examples(args, tokenizer, evaluate=False):
-    file_path = args.eval_data_file if evaluate else args.train_data_file
-    if args.line_by_line:
-        return LineByLineTextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
-    else:
-        return TextDataset(tokenizer, args, file_path=file_path, block_size=args.block_size)
-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 _sorted_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> List[str]:
-    ordering_and_checkpoint_path = []
-    glob_checkpoints = glob.glob(os.path.join(args.output_dir, "{}-*".format(checkpoint_prefix)))
-    for path in glob_checkpoints:
-        if use_mtime:
-            ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
-        else:
-            regex_match = re.match(".*{}-([0-9]+)".format(checkpoint_prefix), path)
-            if regex_match and regex_match.groups():
-                ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
-    checkpoints_sorted = sorted(ordering_and_checkpoint_path)
-    checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
-    return checkpoints_sorted
-def _rotate_checkpoints(args, checkpoint_prefix="checkpoint", use_mtime=False) -> None:
-    if not args.save_total_limit:
-        return
-    if args.save_total_limit <= 0:
-        return
-    # Check if we should delete older checkpoint(s)
-    checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)
-    if len(checkpoints_sorted) <= args.save_total_limit:
-        return
-    number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
-    checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
-    for checkpoint in checkpoints_to_be_deleted:
-        logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
-        shutil.rmtree(checkpoint)
-def mask_tokens(inputs: torch.Tensor, tokenizer: PreTrainedTokenizer, args) -> Tuple[torch.Tensor, torch.Tensor]:
-    """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """
-    if tokenizer.mask_token is None:
-        raise ValueError(
-            "This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the --mlm flag if you want to use this tokenizer."
-        )
-    labels = inputs.clone()
-    # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
-    probability_matrix = torch.full(labels.shape, args.mlm_probability)
-    special_tokens_mask = [
-        tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
-    ]
-    probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
-    if tokenizer._pad_token is not None:
-        padding_mask = labels.eq(tokenizer.pad_token_id)
-        probability_matrix.masked_fill_(padding_mask, value=0.0)
-    masked_indices = torch.bernoulli(probability_matrix).bool()
-    labels[~masked_indices] = -100  # We only compute loss on masked tokens
-    # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
-    indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
-    inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)
-    # 10% of the time, we replace masked input tokens with random word
-    indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
-    random_words = torch.randint(len(tokenizer), labels.shape, dtype=torch.long)
-    inputs[indices_random] = random_words[indices_random]
-    # The rest of the time (10% of the time) we keep the masked input tokens unchanged
-    return inputs, labels
-def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:
-    """ 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)
-    def collate(examples: List[torch.Tensor]):
-        if tokenizer._pad_token is None:
-            return pad_sequence(examples, batch_first=True)
-        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
-    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, collate_fn=collate
-    )
-    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
-    model = model.module if hasattr(model, "module") else model  # Take care of distributed/parallel training
-    model.resize_token_embeddings(len(tokenizer))
-    # 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
-    )
-    # Check if saved optimizer or scheduler states exist
-    if (
-        args.model_name_or_path
-        and os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt"))
-        and os.path.isfile(os.path.join(args.model_name_or_path, "scheduler.pt"))
-    ):
-        # Load in optimizer and scheduler states
-        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
-        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
-    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
-    epochs_trained = 0
-    steps_trained_in_current_epoch = 0
-    # Check if continuing training from a checkpoint
-    if args.model_name_or_path and os.path.exists(args.model_name_or_path):
-        try:
-            # set global_step to gobal_step of last saved checkpoint from model path
-            checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
-            global_step = int(checkpoint_suffix)
-            epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
-            steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
-            logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-            logger.info("  Continuing training from epoch %d", epochs_trained)
-            logger.info("  Continuing training from global step %d", global_step)
-            logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
-        except ValueError:
-            logger.info("  Starting fine-tuning.")
-    tr_loss, logging_loss = 0.0, 0.0
-    model.zero_grad()
-    train_iterator = trange(
-        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
-    )
-    set_seed(args)  # Added here for reproducibility
-    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):
-            # Skip past any already trained steps if resuming training
-            if steps_trained_in_current_epoch > 0:
-                steps_trained_in_current_epoch -= 1
-                continue
-            inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
-            inputs = inputs.to(args.device)
-            labels = labels.to(args.device)
-            model.train()
-            outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
-            loss = outputs[0]  # model outputs are always tuple in 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)
-                optimizer.step()
-                scheduler.step()  # Update learning rate schedule
-                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)
-                        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:
-                    checkpoint_prefix = "checkpoint"
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "{}-{}".format(checkpoint_prefix, global_step))
-                    os.makedirs(output_dir, exist_ok=True)
-                    model_to_save = (
-                        model.module if hasattr(model, "module") else model
-                    )  # Take care of distributed/parallel training
-                    model_to_save.save_pretrained(output_dir)
-                    tokenizer.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
-                    _rotate_checkpoints(args, checkpoint_prefix)
-                    torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
-                    torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
-                    logger.info("Saving optimizer and scheduler states 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 evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, prefix="") -> Dict:
-    # Loop to handle MNLI double evaluation (matched, mis-matched)
-    eval_output_dir = args.output_dir
-    eval_dataset = load_and_cache_examples(args, tokenizer, evaluate=True)
-    if args.local_rank in [-1, 0]:
-        os.makedirs(eval_output_dir, exist_ok=True)
-    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-    # Note that DistributedSampler samples randomly
-    def collate(examples: List[torch.Tensor]):
-        if tokenizer._pad_token is None:
-            return pad_sequence(examples, batch_first=True)
-        return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)
-    eval_sampler = SequentialSampler(eval_dataset)
-    eval_dataloader = DataLoader(
-        eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate
-    )
-    # multi-gpu evaluate
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
-    # Eval!
-    logger.info("***** Running evaluation {} *****".format(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
-    model.eval()
-    for batch in tqdm(eval_dataloader, desc="Evaluating"):
-        inputs, labels = mask_tokens(batch, tokenizer, args) if args.mlm else (batch, batch)
-        inputs = inputs.to(args.device)
-        labels = labels.to(args.device)
-        with torch.no_grad():
-            outputs = model(inputs, masked_lm_labels=labels) if args.mlm else model(inputs, labels=labels)
-            lm_loss = outputs[0]
-            eval_loss += lm_loss.mean().item()
-        nb_eval_steps += 1
-    eval_loss = eval_loss / nb_eval_steps
-    perplexity = torch.exp(torch.tensor(eval_loss))
-    result = {"perplexity": perplexity}
-    output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
-    with open(output_eval_file, "w") as writer:
-        logger.info("***** Eval results {} *****".format(prefix))
-        for key in sorted(result.keys()):
-            logger.info("  %s = %s", key, str(result[key]))
-            writer.write("%s = %s\n" % (key, str(result[key])))
-    return result
-def main():
-    parser = argparse.ArgumentParser()
-    # Required parameters
-    parser.add_argument(
-        "--train_data_file", default=None, type=str, required=True, help="The input training data file (a text file)."
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        required=True,
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--model_type", type=str, required=True, help="The model architecture to be trained or fine-tuned.",
-    )
-    # Other parameters
-    parser.add_argument(
-        "--eval_data_file",
-        default=None,
-        type=str,
-        help="An optional input evaluation data file to evaluate the perplexity on (a text file).",
-    )
-    parser.add_argument(
-        "--line_by_line",
-        action="store_true",
-        help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
-    )
-    parser.add_argument(
-        "--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir"
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        default=None,
-        type=str,
-        help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",
-    )
-    parser.add_argument(
-        "--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling."
-    )
-    parser.add_argument(
-        "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
-    )
-    parser.add_argument(
-        "--config_name",
-        default=None,
-        type=str,
-        help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default=None,
-        type=str,
-        help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        default=None,
-        type=str,
-        help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",
-    )
-    parser.add_argument(
-        "--block_size",
-        default=-1,
-        type=int,
-        help="Optional input sequence length after tokenization."
-        "The training dataset will be truncated in block of this size for training."
-        "Default to the model max input length for single sentence inputs (take into account special tokens).",
-    )
-    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(
-        "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
-    )
-    parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.")
-    parser.add_argument(
-        "--per_gpu_eval_batch_size", default=4, 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=1.0, 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=500, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--save_total_limit",
-        type=int,
-        default=None,
-        help="Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default",
-    )
-    parser.add_argument(
-        "--eval_all_checkpoints",
-        action="store_true",
-        help="Evaluate all checkpoints starting with the same prefix as model_name_or_path 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.")
-    args = parser.parse_args()
-    if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
-        raise ValueError(
-            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "
-            "flag (masked language modeling)."
-        )
-    if args.eval_data_file is None and args.do_eval:
-        raise ValueError(
-            "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
-            "or remove the --do_eval argument."
-        )
-    if args.should_continue:
-        sorted_checkpoints = _sorted_checkpoints(args)
-        if len(sorted_checkpoints) == 0:
-            raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.")
-        else:
-            args.model_name_or_path = sorted_checkpoints[-1]
-    if (
-        os.path.exists(args.output_dir)
-        and os.listdir(args.output_dir)
-        and args.do_train
-        and not args.overwrite_output_dir
-        and not args.should_continue
-    ):
-        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" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = 0 if args.no_cuda else 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
-    # Setup logging
-    logging.basicConfig(
-        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)
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training download model & vocab
-    if args.config_name:
-        config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)
-    elif args.model_name_or_path:
-        config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
-    else:
-        # When we release a pip version exposing CONFIG_MAPPING,
-        # we can do `config = CONFIG_MAPPING[args.model_type]()`.
-        raise ValueError(
-            "You are instantiating a new config instance from scratch. This is not supported, but you can do it from another script, save it,"
-            "and load it from here, using --config_name"
-        )
-    if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)
-    elif args.model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
-    else:
-        raise ValueError(
-            "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,"
-            "and load it from here, using --tokenizer_name"
-        )
-    if args.block_size <= 0:
-        args.block_size = tokenizer.max_len
-        # Our input block size will be the max possible for the model
-    else:
-        args.block_size = min(args.block_size, tokenizer.max_len)
-    if args.model_name_or_path:
-        model = AutoModelWithLMHead.from_pretrained(
-            args.model_name_or_path,
-            from_tf=bool(".ckpt" in args.model_name_or_path),
-            config=config,
-            cache_dir=args.cache_dir,
-        )
-    else:
-        logger.info("Training new model from scratch")
-        model = AutoModelWithLMHead.from_config(config)
-    model.to(args.device)
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # End of barrier to make sure only the first process in distributed training download model & vocab
-    logger.info("Training/evaluation parameters %s", args)
-    # Training
-    if args.do_train:
-        if args.local_rank not in [-1, 0]:
-            torch.distributed.barrier()  # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
-        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)
-        if args.local_rank == 0:
-            torch.distributed.barrier()
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-    # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir, exist_ok=True)
-        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"))
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = AutoModelWithLMHead.from_pretrained(args.output_dir)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
-        model.to(args.device)
-    # Evaluation
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        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("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 ""
-            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
-            model = AutoModelWithLMHead.from_pretrained(checkpoint)
-            model.to(args.device)
-            result = evaluate(args, model, tokenizer, prefix=prefix)
-            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
-            results.update(result)
-    return results
-if __name__ == "__main__":
-    main()