run_translation.py 28.7 KB
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#!/usr/bin/env python
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# coding=utf-8
# Copyright The HuggingFace Team and The HuggingFace Inc. team. 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 sequence to sequence.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

import logging
import os
import sys
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import warnings
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from dataclasses import dataclass, field
from typing import Optional

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import datasets
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import evaluate
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import numpy as np
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from datasets import load_dataset
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import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
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    M2M100Tokenizer,
    MBart50Tokenizer,
    MBart50TokenizerFast,
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    MBartTokenizer,
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    MBartTokenizerFast,
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    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    default_data_collator,
    set_seed,
)
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from transformers.trainer_utils import get_last_checkpoint
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from transformers.utils import check_min_version, send_example_telemetry
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from transformers.utils.versions import require_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.32.0.dev0")
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
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logger = logging.getLogger(__name__)

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# A list of all multilingual tokenizer which require src_lang and tgt_lang attributes.
MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer]

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@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
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    token: str = field(
        default=None,
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        metadata={
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            "help": (
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                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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            )
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        },
    )
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    use_auth_token: bool = field(
        default=None,
        metadata={
            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
        },
    )
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@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

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    source_lang: str = field(default=None, metadata={"help": "Source language id for translation."})
    target_lang: str = field(default=None, metadata={"help": "Target language id for translation."})

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    dataset_name: Optional[str] = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
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    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a jsonlines)."})
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    validation_file: Optional[str] = field(
        default=None,
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        metadata={
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            "help": "An optional input evaluation data file to evaluate the metrics (sacrebleu) on a jsonlines file."
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        },
    )
    test_file: Optional[str] = field(
        default=None,
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        metadata={"help": "An optional input test data file to evaluate the metrics (sacrebleu) on a jsonlines file."},
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    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_source_length: Optional[int] = field(
        default=1024,
        metadata={
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            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
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        },
    )
    max_target_length: Optional[int] = field(
        default=128,
        metadata={
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            "help": (
                "The maximum total sequence length for target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
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        },
    )
    val_max_target_length: Optional[int] = field(
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        default=None,
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        metadata={
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            "help": (
                "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
                "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                "during ``evaluate`` and ``predict``."
            )
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        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
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            "help": (
                "Whether to pad all samples to model maximum sentence length. "
                "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                "efficient on GPU but very bad for TPU."
            )
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        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
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            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
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        },
    )
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    max_eval_samples: Optional[int] = field(
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        default=None,
        metadata={
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            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
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        },
    )
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    max_predict_samples: Optional[int] = field(
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        default=None,
        metadata={
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            "help": (
                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                "value if set."
            )
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        },
    )
    num_beams: Optional[int] = field(
        default=None,
        metadata={
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            "help": (
                "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                "which is used during ``evaluate`` and ``predict``."
            )
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        },
    )
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    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )
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    source_prefix: Optional[str] = field(
        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
    )
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    forced_bos_token: Optional[str] = field(
        default=None,
        metadata={
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            "help": (
                "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for"
                " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to"
                " be the target language token.(Usually it is the target language token)"
            )
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        },
    )
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    def __post_init__(self):
        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
            raise ValueError("Need either a dataset name or a training/validation file.")
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        elif self.source_lang is None or self.target_lang is None:
            raise ValueError("Need to specify the source language and the target language.")

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        # accepting both json and jsonl file extensions, as
        # many jsonlines files actually have a .json extension
        valid_extensions = ["json", "jsonl"]

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        if self.train_file is not None:
            extension = self.train_file.split(".")[-1]
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            assert extension in valid_extensions, "`train_file` should be a jsonlines file."
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        if self.validation_file is not None:
            extension = self.validation_file.split(".")[-1]
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            assert extension in valid_extensions, "`validation_file` should be a jsonlines file."
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        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length
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def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

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    if model_args.use_auth_token is not None:
        warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
        if model_args.token is not None:
            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
        model_args.token = model_args.use_auth_token

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    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_translation", model_args, data_args)

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    # Setup logging
    logging.basicConfig(
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        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
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    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

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    log_level = training_args.get_process_log_level()
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    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
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    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
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    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
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    )
    logger.info(f"Training/evaluation parameters {training_args}")

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    if data_args.source_prefix is None and model_args.model_name_or_path in [
        "t5-small",
        "t5-base",
        "t5-large",
        "t5-3b",
        "t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "
            "`--source_prefix 'translate English to German: ' `"
        )

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    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
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        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )
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    # Set seed before initializing model.
    set_seed(training_args.seed)

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    # Get the datasets: you can either provide your own JSON training and evaluation files (see below)
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    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For translation, only JSON files are supported, with one field named "translation" containing two keys for the
    # source and target languages (unless you adapt what follows).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
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        raw_datasets = load_dataset(
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            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
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            token=model_args.token,
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        )
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    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
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        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
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        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
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            token=model_args.token,
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        )
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    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
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        token=model_args.token,
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    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
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        token=model_args.token,
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    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
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        token=model_args.token,
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    )

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    # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
    # on a small vocab and want a smaller embedding size, remove this test.
    embedding_size = model.get_input_embeddings().weight.shape[0]
    if len(tokenizer) > embedding_size:
        model.resize_token_embeddings(len(tokenizer))
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    # Set decoder_start_token_id
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    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
        if isinstance(tokenizer, MBartTokenizer):
            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
        else:
            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)

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    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

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    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
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    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
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        column_names = raw_datasets["train"].column_names
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    elif training_args.do_eval:
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        column_names = raw_datasets["validation"].column_names
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    elif training_args.do_predict:
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        column_names = raw_datasets["test"].column_names
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    else:
        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
        return
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    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
    # ignore those attributes).
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    if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
        assert data_args.target_lang is not None and data_args.source_lang is not None, (
            f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
            "--target_lang arguments."
        )

        tokenizer.src_lang = data_args.source_lang
        tokenizer.tgt_lang = data_args.target_lang

        # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
        # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
        forced_bos_token_id = (
            tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
        )
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        model.config.forced_bos_token_id = forced_bos_token_id
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    # Get the language codes for input/target.
    source_lang = data_args.source_lang.split("_")[0]
    target_lang = data_args.target_lang.split("_")[0]
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    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

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    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
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        logger.warning(
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            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

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    def preprocess_function(examples):
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        inputs = [ex[source_lang] for ex in examples["translation"]]
        targets = [ex[target_lang] for ex in examples["translation"]]
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        inputs = [prefix + inp for inp in inputs]
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        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)

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        # Tokenize targets with the `text_target` keyword argument
        labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
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        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            labels["input_ids"] = [
                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
            ]

        model_inputs["labels"] = labels["input_ids"]
        return model_inputs

    if training_args.do_train:
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        if "train" not in raw_datasets:
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            raise ValueError("--do_train requires a train dataset")
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        train_dataset = raw_datasets["train"]
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        if data_args.max_train_samples is not None:
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            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))
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        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on train dataset",
            )
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    if training_args.do_eval:
        max_target_length = data_args.val_max_target_length
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        if "validation" not in raw_datasets:
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            raise ValueError("--do_eval requires a validation dataset")
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        eval_dataset = raw_datasets["validation"]
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        if data_args.max_eval_samples is not None:
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            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
            eval_dataset = eval_dataset.select(range(max_eval_samples))
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        with training_args.main_process_first(desc="validation dataset map pre-processing"):
            eval_dataset = eval_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on validation dataset",
            )
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    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
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        if "test" not in raw_datasets:
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            raise ValueError("--do_predict requires a test dataset")
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        predict_dataset = raw_datasets["test"]
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        if data_args.max_predict_samples is not None:
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            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
            predict_dataset = predict_dataset.select(range(max_predict_samples))
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        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
            predict_dataset = predict_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on prediction dataset",
            )
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    # Data collator
    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    else:
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        data_collator = DataCollatorForSeq2Seq(
            tokenizer,
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            model=model,
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            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=8 if training_args.fp16 else None,
        )
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    # Metric
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    metric = evaluate.load("sacrebleu")
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    def postprocess_text(preds, labels):
        preds = [pred.strip() for pred in preds]
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        labels = [[label.strip()] for label in labels]
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        return preds, labels

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    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]
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        # Replace -100s used for padding as we can't decode them
        preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
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        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
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        labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
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        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

        # Some simple post-processing
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        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
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        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
        result = {"bleu": result["score"]}
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        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
        result["gen_len"] = np.mean(prediction_lens)
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        result = {k: round(v, 4) for k, v in result.items()}
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        return result

    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
    )

    # Training
    if training_args.do_train:
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        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
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            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
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        trainer.save_model()  # Saves the tokenizer too for easy upload

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        metrics = train_result.metrics
        max_train_samples = (
            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
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        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()
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    # Evaluation
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    results = {}
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    max_length = (
        training_args.generation_max_length
        if training_args.generation_max_length is not None
        else data_args.val_max_target_length
    )
    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
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    if training_args.do_eval:
        logger.info("*** Evaluate ***")

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        metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval")
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        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
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        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)
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    if training_args.do_predict:
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        logger.info("*** Predict ***")
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        predict_results = trainer.predict(
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            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
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        )
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        metrics = predict_results.metrics
        max_predict_samples = (
            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
        )
        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
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        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)
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        if trainer.is_world_process_zero():
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            if training_args.predict_with_generate:
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                predictions = predict_results.predictions
                predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id)
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                predictions = tokenizer.batch_decode(
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                    predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
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                )
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                predictions = [pred.strip() for pred in predictions]
                output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
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                with open(output_prediction_file, "w", encoding="utf-8") as writer:
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                    writer.write("\n".join(predictions))
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    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"}
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name
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    languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None]
    if len(languages) > 0:
        kwargs["language"] = languages

    if training_args.push_to_hub:
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        trainer.push_to_hub(**kwargs)
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    else:
        trainer.create_model_card(**kwargs)
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    return results

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def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()