usrbinenv python codingutf8 the huggingface team and the huggingface inc team 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 orglicenseslicense2 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 finetuning the library models for multiple choice you can also adapt this script on your own multiple choice task pointers for this are left as comments will error if the minimal version of transformers is not installed remove at your own risks region helper classes and functions data collator that will dynamically pad the inputs for multiple choice received args tokenizer pretrainedtokenizer or pretrainedtokenizerfast the tokenizer used for encoding the data padding bool str or utils paddingstrategy optional defaults to true select a strategy to pad the returned sequences according to the model s padding side and padding index among true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided maxlength pad to a maximum length specified with the argument maxlength or to the maximum acceptable input length for the model if that argument is not provided false or donotpad default no padding i e can output a batch with sequences of different lengths maxlength int optional maximum length of the returned list and optionally padding length see above padtomultipleof int optional if set will pad the sequence to a multiple of the provided value this is especially useful to enable the use of tensor cores on nvidia hardware with compute capability 7 5 volta unflatten add back labels endregion region arguments arguments pertaining to which modelconfigtokenizer we are going to finetune from arguments pertaining to what data we are going to input our model for training and eval endregion region argument parsing see all possible arguments in srctransformerstrainingargs py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns 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 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 pythonpytorch versions endregion region logging endregion region checkpoints endregion set seed before initializing model region load datasets get the datasets you can either provide your own csvjsontxt training and evaluation files see below or just provide the name of one of the public datasets available on the hub at https huggingface codatasets the dataset will be downloaded automatically from the datasets hub for csvjson files this script will use the column called text or the first column if no column called text is found you can easily tweak this behavior see below in distributed training the loaddataset function guarantee that only one local process can concurrently download the dataset downloading and loading the swag dataset from the hub see more about loading any type of standard or custom dataset from files python dict pandas dataframe etc at https huggingface codocsdatasetsloadingdatasets when using your own dataset or a different dataset from swag you will probably need to change this endregion region load model config and tokenizer distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab endregion region dataset preprocessing flatten out tokenize unflatten custom class defined above as hf has no data collator for multiple choice endregion region build model transformers models compute the right loss for their task by default when labels are passed and will use this for training unless you specify your own loss function in compile endregion region preparing pushtohub and model card endregion region training model preparetfdataset wraps a hugging face dataset in a tf data dataset which is ready to use in training this is the recommended way to use a hugging face dataset when training with keras you can also use the lowerlevel dataset totfdataset method but you will have to specify things like column names yourself if you use this method whereas they are automatically inferred from the model input names when using model preparetfdataset for more info see the docs https huggingface codocstransformersmainenmainclassesmodeltransformers tfpretrainedmodel preparetfdataset https huggingface codocsdatasetsmainenpackagereferencemainclassesdatasets dataset totfdataset endregion region evaluation do a standalone evaluation pass endregion region push to hub if we re not pushing to hub at least save a local copy when we re done endregion usr bin env python coding utf 8 the huggingface team and the huggingface inc team 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 multiple choice you can also adapt this script on your own multiple choice task pointers for this are left as comments will error if the minimal version of transformers is not installed remove at your own risks region helper classes and functions data collator that will dynamically pad the inputs for multiple choice received args tokenizer pretrainedtokenizer or pretrainedtokenizerfast the tokenizer used for encoding the data padding bool str or utils paddingstrategy optional defaults to true select a strategy to pad the returned sequences according to the model s padding side and padding index among true or longest pad to the longest sequence in the batch or no padding if only a single sequence if provided max_length pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided false or do_not_pad default no padding i e can output a batch with sequences of different lengths max_length int optional maximum length of the returned list and optionally padding length see above pad_to_multiple_of int optional if set will pad the sequence to a multiple of the provided value this is especially useful to enable the use of tensor cores on nvidia hardware with compute capability 7 5 volta un flatten add back labels endregion region arguments arguments pertaining to which model config tokenizer we are going to fine tune from arguments pertaining to what data we are going to input our model for training and eval endregion region argument parsing 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 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 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 endregion region logging endregion region checkpoints endregion set seed before initializing model region load datasets get the datasets you can either provide your own csv json txt training and evaluation files see below 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 csv json files this script will use the column called text or the first column if no column called text is found you can easily tweak this behavior see below in distributed training the load_dataset function guarantee that only one local process can concurrently download the dataset downloading and loading the swag dataset from the hub 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 when using your own dataset or a different dataset from swag you will probably need to change this endregion region load model config and tokenizer distributed training the from_pretrained methods guarantee that only one local process can concurrently download model vocab endregion region dataset preprocessing flatten out tokenize un flatten custom class defined above as hf has no data collator for multiple choice endregion region build model transformers models compute the right loss for their task by default when labels are passed and will use this for training unless you specify your own loss function in compile endregion region preparing push_to_hub and model card endregion region training model prepare_tf_dataset wraps a hugging face dataset in a tf data dataset which is ready to use in training this is the recommended way to use a hugging face dataset when training with keras you can also use the lower level dataset to_tf_dataset method but you will have to specify things like column names yourself if you use this method whereas they are automatically inferred from the model input names when using model prepare_tf_dataset for more info see the docs https huggingface co docs transformers main en main_classes model transformers tfpretrainedmodel prepare_tf_dataset https huggingface co docs datasets main en package_reference main_classes datasets dataset to_tf_dataset endregion region evaluation do a standalone evaluation pass endregion region push to hub if we re not pushing to hub at least save a local copy when we re done endregion
import json
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from itertools import chain
from pathlib import Path
from typing import Optional, Union

import datasets
import tensorflow as tf
from datasets import load_dataset

import transformers
from transformers import (
    CONFIG_NAME,
    TF2_WEIGHTS_NAME,
    AutoConfig,
    AutoTokenizer,
    DefaultDataCollator,
    HfArgumentParser,
    PushToHubCallback,
    TFAutoModelForMultipleChoice,
    TFTrainingArguments,
    create_optimizer,
    set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry



check_min_version("4.36.0.dev0")

logger = logging.getLogger(__name__)





@dataclass
class DataCollatorForMultipleChoice:
    

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str, PaddingStrategy] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature.pop(label_name) for feature in features]
        batch_size = len(features)
        num_choices = len(features[0]["input_ids"])
        flattened_features = [
            [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
        ]
        flattened_features = list(chain(*flattened_features))

        batch = self.tokenizer.pad(
            flattened_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="np",
        )

        
        batch = {k: tf.reshape(v, (batch_size, num_choices, -1)) for k, v in batch.items()}
        
        batch["labels"] = tf.convert_to_tensor(labels, dtype=tf.int64)
        return batch






@dataclass
class ModelArguments:
    

    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 do you want 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)."},
    )
    token: str = field(
        default=None,
        metadata={
            "help": (
                "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`)."
            )
        },
    )
    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` instead."
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
                "execute code present on the Hub on your local machine."
            )
        },
    )


@dataclass
class DataTrainingArguments:
    

    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
    )
    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_seq_length: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. If passed, sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to pad all samples to the 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."
            )
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )

    def __post_init__(self):
        if self.train_file is not None:
            extension = self.train_file.split(".")[-1]
            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
        if self.validation_file is not None:
            extension = self.validation_file.split(".")[-1]
            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."





def main():
    
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        
        
        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()

    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. Please use `token` instead.",
            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

    
    
    send_example_telemetry("run_swag", model_args, data_args, framework="tensorflow")

    output_dir = Path(training_args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    

    
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
    

    
    checkpoint = None
    if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
        if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file():
            checkpoint = output_dir
            logger.info(
                f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
                " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )
        else:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to continue regardless."
            )
    

    
    set_seed(training_args.seed)

    
    
    
    

    
    

    
    
    if data_args.train_file is not None or data_args.validation_file is not None:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = data_args.train_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    else:
        
        raw_datasets = load_dataset(
            "swag",
            "regular",
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    
    

    
    ending_names = [f"ending{i}" for i in range(4)]
    context_name = "sent1"
    question_header_name = "sent2"
    

    
    if checkpoint is not None:
        config_path = training_args.output_dir
    elif model_args.config_name:
        config_path = model_args.config_name
    else:
        config_path = model_args.model_name_or_path

    
    
    
    config = AutoConfig.from_pretrained(
        config_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    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,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    

    
    if data_args.max_seq_length is None:
        max_seq_length = tokenizer.model_max_length
        if max_seq_length > 1024:
            logger.warning(
                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
            )
            max_seq_length = 1024
    else:
        if data_args.max_seq_length > tokenizer.model_max_length:
            logger.warning(
                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
            )
        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    def preprocess_function(examples):
        first_sentences = [[context] * 4 for context in examples[context_name]]
        question_headers = examples[question_header_name]
        second_sentences = [
            [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
        ]

        
        first_sentences = list(chain(*first_sentences))
        second_sentences = list(chain(*second_sentences))

        
        tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True, max_length=max_seq_length)
        
        data = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
        return data

    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets["train"]
        if data_args.max_train_samples is not None:
            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
            train_dataset = train_dataset.select(range(max_train_samples))
        train_dataset = train_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_eval:
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets["validation"]
        if data_args.max_eval_samples is not None:
            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
            eval_dataset = eval_dataset.select(range(max_eval_samples))
        eval_dataset = eval_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if data_args.pad_to_max_length:
        data_collator = DefaultDataCollator(return_tensors="np")
    else:
        
        data_collator = DataCollatorForMultipleChoice(tokenizer)
    

    with training_args.strategy.scope():
        
        if checkpoint is None:
            model_path = model_args.model_name_or_path
        else:
            model_path = checkpoint
        model = TFAutoModelForMultipleChoice.from_pretrained(
            model_path,
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            token=model_args.token,
            trust_remote_code=model_args.trust_remote_code,
        )

        num_replicas = training_args.strategy.num_replicas_in_sync
        total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
        total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas

        if training_args.do_train:
            num_train_steps = (len(train_dataset) // total_train_batch_size) * int(training_args.num_train_epochs)
            if training_args.warmup_steps > 0:
                num_warmup_steps = training_args.warmup_steps
            elif training_args.warmup_ratio > 0:
                num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
            else:
                num_warmup_steps = 0
            optimizer, lr_schedule = create_optimizer(
                init_lr=training_args.learning_rate,
                num_train_steps=num_train_steps,
                num_warmup_steps=num_warmup_steps,
                adam_beta1=training_args.adam_beta1,
                adam_beta2=training_args.adam_beta2,
                adam_epsilon=training_args.adam_epsilon,
                weight_decay_rate=training_args.weight_decay,
                adam_global_clipnorm=training_args.max_grad_norm,
            )
        else:
            optimizer = None
        
        
        model.compile(optimizer=optimizer, metrics=["accuracy"], jit_compile=training_args.xla)
        

        
        push_to_hub_model_id = training_args.push_to_hub_model_id
        model_name = model_args.model_name_or_path.split("/")[-1]
        if not push_to_hub_model_id:
            push_to_hub_model_id = f"{model_name}-finetuned-multiplechoice"

        model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "multiple-choice"}

        if training_args.push_to_hub:
            callbacks = [
                PushToHubCallback(
                    output_dir=training_args.output_dir,
                    hub_model_id=push_to_hub_model_id,
                    hub_token=training_args.push_to_hub_token,
                    tokenizer=tokenizer,
                    **model_card_kwargs,
                )
            ]
        else:
            callbacks = []
        

        
        eval_metrics = None
        if training_args.do_train:
            dataset_options = tf.data.Options()
            dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF

            
            
            
            
            
            
            
            

            tf_train_dataset = model.prepare_tf_dataset(
                train_dataset,
                shuffle=True,
                batch_size=total_train_batch_size,
                collate_fn=data_collator,
            ).with_options(dataset_options)

            if training_args.do_eval:
                validation_data = model.prepare_tf_dataset(
                    eval_dataset,
                    shuffle=False,
                    batch_size=total_eval_batch_size,
                    collate_fn=data_collator,
                    drop_remainder=True,
                ).with_options(dataset_options)
            else:
                validation_data = None
            history = model.fit(
                tf_train_dataset,
                validation_data=validation_data,
                epochs=int(training_args.num_train_epochs),
                callbacks=callbacks,
            )
            eval_metrics = {key: val[-1] for key, val in history.history.items()}
        

        
        if training_args.do_eval and not training_args.do_train:
            dataset_options = tf.data.Options()
            dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
            
            tf_eval_dataset = model.prepare_tf_dataset(
                eval_dataset,
                shuffle=False,
                batch_size=total_eval_batch_size,
                collate_fn=data_collator,
                drop_remainder=True,
            ).with_options(dataset_options)
            eval_results = model.evaluate(tf_eval_dataset)
            eval_metrics = {"val_loss": eval_results[0], "val_accuracy": eval_results[1]}
        

        if eval_metrics is not None and training_args.output_dir is not None:
            output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
            with open(output_eval_file, "w") as writer:
                writer.write(json.dumps(eval_metrics))

        

        if training_args.output_dir is not None and not training_args.push_to_hub:
            
            model.save_pretrained(training_args.output_dir)
        


if __name__ == "__main__":
    main()