usrbinenv python codingutf8 2020 the huggingface 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 xlnet for question answering with beam search using a slightly adapted version of the trainer you can also adapt this script on your own question answering task pointers for this are left as comments will error if the minimal version of transformers is not installed remove at your own risks 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 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 setup logging the default of trainingargs loglevel is passive so we set log level at info here to have that default log on each process the small summary detecting last checkpoint set seed before initializing model 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 a 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 load pretrained model and tokenizer distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab preprocessing the datasets preprocessing is slighlty different for training and evaluation padding side determines if we do questioncontext or contextquestion training preprocessing some of the questions have lots of whitespace on the left which is not useful and will make the truncation of the context fail the tokenized question will take a lots of space so we remove that left whitespace tokenize our examples with truncation and maybe padding but keep the overflows using a stride this results in one example possible giving several features when a context is long each of those features having a context that overlaps a bit the context of the previous feature since one example might give us several features if it has a long context we need a map from a feature to its corresponding example this key gives us just that the offset mappings will give us a map from token to character position in the original context this will help us compute the startpositions and endpositions the special tokens will help us build the pmask which indicates the tokens that can t be in answers let s label those examples we will label impossible answers with the index of the cls token grab the sequence corresponding to that example to know what is the context and what is the question build the pmask non special tokens and context gets 0 0 the others get 1 0 the cls token gets 1 0 too for predictions of empty answers one example can give several spans this is the index of the example containing this span of text if no answers are given set the clsindex as answer startend character index of the answer in the text start token index of the current span in the text end token index of the current span in the text detect if the answer is out of the span in which case this feature is labeled with the cls index otherwise move the tokenstartindex and tokenendindex to the two ends of the answer note we could go after the last offset if the answer is the last word edge case select samples from dataset this will help to decrease processing time create training features select samples from dataset again since feature creation might increase number of features validation preprocessing tokenize our examples with truncation and maybe padding but keep the overflows using a stride this results in one example possible giving several features when a context is long each of those features having a context that overlaps a bit the context of the previous feature since one example might give us several features if it has a long context we need a map from a feature to its corresponding example this key gives us just that the special tokens will help us build the pmask which indicates the tokens that can t be in answers for evaluation we will need to convert our predictions to substrings of the context so we keep the corresponding exampleid and we will store the offset mappings we still provide the index of the cls token and the pmask to the model but not the isimpossible label find the cls token in the input ids grab the sequence corresponding to that example to know what is the context and what is the question build the pmask non special tokens and context gets 0 0 the others 1 0 one example can give several spans this is the index of the example containing this span of text set to none the offsetmapping that are not part of the context so it s easy to determine if a token position is part of the context or not selecting eval samples from dataset create features from eval dataset selecting samples from dataset again since feature creation might increase samples size we will select sample from whole data test feature creation during feature creation dataset samples might increase we will select required samples again data collator we have already padded to max length if the corresponding flag is true otherwise we need to pad in the data collator postprocessing postprocessing we match the start logits and end logits to answers in the original context format the result to the format the metric expects initialize our trainer training evaluation prediction for xlaspawn tpus usr bin env python coding utf 8 2020 the huggingface 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 xlnet for question answering with beam search using a slightly adapted version of the trainer you can also adapt this script on your own question answering task pointers for this are left as comments will error if the minimal version of transformers is not installed remove at your own risks 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 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 setup logging the default of training_args log_level is passive so we set log level at info here to have that default log on each process the small summary detecting last checkpoint set seed before initializing model 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 a 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 load pretrained model and tokenizer distributed training the from_pretrained methods guarantee that only one local process can concurrently download model vocab preprocessing the datasets preprocessing is slighlty different for training and evaluation padding side determines if we do question context or context question training preprocessing some of the questions have lots of whitespace on the left which is not useful and will make the truncation of the context fail the tokenized question will take a lots of space so we remove that left whitespace tokenize our examples with truncation and maybe padding but keep the overflows using a stride this results in one example possible giving several features when a context is long each of those features having a context that overlaps a bit the context of the previous feature since one example might give us several features if it has a long context we need a map from a feature to its corresponding example this key gives us just that the offset mappings will give us a map from token to character position in the original context this will help us compute the start_positions and end_positions the special tokens will help us build the p_mask which indicates the tokens that can t be in answers let s label those examples we will label impossible answers with the index of the cls token grab the sequence corresponding to that example to know what is the context and what is the question build the p_mask non special tokens and context gets 0 0 the others get 1 0 the cls token gets 1 0 too for predictions of empty answers one example can give several spans this is the index of the example containing this span of text if no answers are given set the cls_index as answer start end character index of the answer in the text start token index of the current span in the text end token index of the current span in the text detect if the answer is out of the span in which case this feature is labeled with the cls index otherwise move the token_start_index and token_end_index to the two ends of the answer note we could go after the last offset if the answer is the last word edge case select samples from dataset this will help to decrease processing time create training features select samples from dataset again since feature creation might increase number of features validation preprocessing tokenize our examples with truncation and maybe padding but keep the overflows using a stride this results in one example possible giving several features when a context is long each of those features having a context that overlaps a bit the context of the previous feature since one example might give us several features if it has a long context we need a map from a feature to its corresponding example this key gives us just that the special tokens will help us build the p_mask which indicates the tokens that can t be in answers for evaluation we will need to convert our predictions to substrings of the context so we keep the corresponding example_id and we will store the offset mappings we still provide the index of the cls token and the p_mask to the model but not the is_impossible label find the cls token in the input ids grab the sequence corresponding to that example to know what is the context and what is the question build the p_mask non special tokens and context gets 0 0 the others 1 0 one example can give several spans this is the index of the example containing this span of text set to none the offset_mapping that are not part of the context so it s easy to determine if a token position is part of the context or not selecting eval samples from dataset create features from eval dataset selecting samples from dataset again since feature creation might increase samples size we will select sample from whole data test feature creation during feature creation dataset samples might increase we will select required samples again data collator we have already padded to max length if the corresponding flag is true otherwise we need to pad in the data collator post processing post processing we match the start logits and end logits to answers in the original context format the result to the format the metric expects initialize our trainer training saves the tokenizer too for easy upload evaluation prediction for xla_spawn tpus
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
from datasets import load_dataset
from trainer_qa import QuestionAnsweringTrainer
from utils_qa import postprocess_qa_predictions_with_beam_search

import transformers
from transformers import (
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TrainingArguments,
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetTokenizerFast,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version



check_min_version("4.36.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")

logger = logging.getLogger(__name__)


@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"},
    )
    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."
        },
    )


@dataclass
class DataTrainingArguments:
    

    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)."}
    )
    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)."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "An optional input test data file to test 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: int = field(
        default=384,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    pad_to_max_length: bool = field(
        default=True,
        metadata={
            "help": (
                "Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when"
                " batching to the maximum length in the batch (which can be faster on GPU but will be slower on 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."
            )
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
                "value if set."
            )
        },
    )
    version_2_with_negative: bool = field(
        default=False, metadata={"help": "If true, some of the examples do not have an answer."}
    )
    null_score_diff_threshold: float = field(
        default=0.0,
        metadata={
            "help": (
                "The threshold used to select the null answer: if the best answer has a score that is less than "
                "the score of the null answer minus this threshold, the null answer is selected for this example. "
                "Only useful when `version_2_with_negative=True`."
            )
        },
    )
    doc_stride: int = field(
        default=128,
        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
    )
    n_best_size: int = field(
        default=20,
        metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
    )
    max_answer_length: int = field(
        default=30,
        metadata={
            "help": (
                "The maximum length of an answer that can be generated. This is needed because the start "
                "and end predictions are not conditioned on one another."
            )
        },
    )

    def __post_init__(self):
        if (
            self.dataset_name is None
            and self.train_file is None
            and self.validation_file is None
            and self.test_file is None
        ):
            raise ValueError("Need either a dataset name or a training/validation/test file.")
        else:
            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."
            if self.test_file is not None:
                extension = self.test_file.split(".")[-1]
                assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."


def main():
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    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_qa_beam_search", model_args, data_args)

    
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        
        transformers.utils.logging.set_verbosity_info()

    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()

    
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    
    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."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            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."
            )

    
    set_seed(training_args.seed)

    
    
    
    
    
    
    
    
    
    if data_args.dataset_name is not None:
        
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    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]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            field="data",
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
    
    

    
    
    
    
    
    config = XLNetConfig.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,
        token=model_args.token,
    )
    tokenizer = XLNetTokenizerFast.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
    )
    model = XLNetForQuestionAnswering.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,
        token=model_args.token,
    )

    
    
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        column_names = raw_datasets["validation"].column_names
    else:
        column_names = raw_datasets["test"].column_names
    question_column_name = "question" if "question" in column_names else column_names[0]
    context_column_name = "context" if "context" in column_names else column_names[1]
    answer_column_name = "answers" if "answers" in column_names else column_names[2]

    
    pad_on_right = tokenizer.padding_side == "right"

    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 prepare_train_features(examples):
        
        
        
        examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]

        
        
        
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            return_special_tokens_mask=True,
            return_token_type_ids=True,
            padding="max_length",
        )

        
        
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        
        
        offset_mapping = tokenized_examples.pop("offset_mapping")
        
        special_tokens = tokenized_examples.pop("special_tokens_mask")

        
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []
        tokenized_examples["is_impossible"] = []
        tokenized_examples["cls_index"] = []
        tokenized_examples["p_mask"] = []

        for i, offsets in enumerate(offset_mapping):
            
            input_ids = tokenized_examples["input_ids"][i]
            cls_index = input_ids.index(tokenizer.cls_token_id)
            tokenized_examples["cls_index"].append(cls_index)

            
            sequence_ids = tokenized_examples["token_type_ids"][i]
            for k, s in enumerate(special_tokens[i]):
                if s:
                    sequence_ids[k] = 3
            context_idx = 1 if pad_on_right else 0

            
            
            tokenized_examples["p_mask"].append(
                [
                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
                    for k, s in enumerate(sequence_ids)
                ]
            )

            
            sample_index = sample_mapping[i]
            answers = examples[answer_column_name][sample_index]
            
            if len(answers["answer_start"]) == 0:
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
                tokenized_examples["is_impossible"].append(1.0)
            else:
                
                start_char = answers["answer_start"][0]
                end_char = start_char + len(answers["text"][0])

                
                token_start_index = 0
                while sequence_ids[token_start_index] != context_idx:
                    token_start_index += 1

                
                token_end_index = len(input_ids) - 1
                while sequence_ids[token_end_index] != context_idx:
                    token_end_index -= 1
                
                if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
                    tokenized_examples["start_positions"].append(cls_index)
                    tokenized_examples["end_positions"].append(cls_index)
                    tokenized_examples["is_impossible"].append(1.0)
                else:
                    
                    
                    while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
                        token_start_index += 1
                    tokenized_examples["start_positions"].append(token_start_index - 1)
                    while offsets[token_end_index][1] >= end_char:
                        token_end_index -= 1
                    tokenized_examples["end_positions"].append(token_end_index + 1)
                    tokenized_examples["is_impossible"].append(0.0)

        return tokenized_examples

    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))
        
        with training_args.main_process_first(desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                prepare_train_features,
                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",
            )
        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))

    
    def prepare_validation_features(examples):
        
        
        
        tokenized_examples = tokenizer(
            examples[question_column_name if pad_on_right else context_column_name],
            examples[context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            return_special_tokens_mask=True,
            return_token_type_ids=True,
            padding="max_length",
        )

        
        
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")

        
        special_tokens = tokenized_examples.pop("special_tokens_mask")

        
        
        tokenized_examples["example_id"] = []

        
        tokenized_examples["cls_index"] = []
        tokenized_examples["p_mask"] = []

        for i, input_ids in enumerate(tokenized_examples["input_ids"]):
            
            cls_index = input_ids.index(tokenizer.cls_token_id)
            tokenized_examples["cls_index"].append(cls_index)

            
            sequence_ids = tokenized_examples["token_type_ids"][i]
            for k, s in enumerate(special_tokens[i]):
                if s:
                    sequence_ids[k] = 3
            context_idx = 1 if pad_on_right else 0

            
            tokenized_examples["p_mask"].append(
                [
                    0.0 if (not special_tokens[i][k] and s == context_idx) or k == cls_index else 1.0
                    for k, s in enumerate(sequence_ids)
                ]
            )

            
            sample_index = sample_mapping[i]
            tokenized_examples["example_id"].append(examples["id"][sample_index])

            
            
            tokenized_examples["offset_mapping"][i] = [
                (o if sequence_ids[k] == context_idx else None)
                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
            ]

        return tokenized_examples

    if training_args.do_eval:
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_examples = raw_datasets["validation"]
        if data_args.max_eval_samples is not None:
            
            max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
            eval_examples = eval_examples.select(range(max_eval_samples))
        
        with training_args.main_process_first(desc="validation dataset map pre-processing"):
            eval_dataset = eval_examples.map(
                prepare_validation_features,
                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",
            )
        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))

    if training_args.do_predict:
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_examples = raw_datasets["test"]
        if data_args.max_predict_samples is not None:
            
            predict_examples = predict_examples.select(range(data_args.max_predict_samples))
        
        with training_args.main_process_first(desc="prediction dataset map pre-processing"):
            predict_dataset = predict_examples.map(
                prepare_validation_features,
                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",
            )
        if data_args.max_predict_samples is not None:
            
            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
            predict_dataset = predict_dataset.select(range(max_predict_samples))

    
    
    
    data_collator = (
        default_data_collator
        if data_args.pad_to_max_length
        else DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
    )

    
    def post_processing_function(examples, features, predictions, stage="eval"):
        
        predictions, scores_diff_json = postprocess_qa_predictions_with_beam_search(
            examples=examples,
            features=features,
            predictions=predictions,
            version_2_with_negative=data_args.version_2_with_negative,
            n_best_size=data_args.n_best_size,
            max_answer_length=data_args.max_answer_length,
            start_n_top=model.config.start_n_top,
            end_n_top=model.config.end_n_top,
            output_dir=training_args.output_dir,
            log_level=log_level,
            prefix=stage,
        )
        
        if data_args.version_2_with_negative:
            formatted_predictions = [
                {"id": k, "prediction_text": v, "no_answer_probability": scores_diff_json[k]}
                for k, v in predictions.items()
            ]
        else:
            formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]

        references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
        return EvalPrediction(predictions=formatted_predictions, label_ids=references)

    metric = evaluate.load("squad_v2" if data_args.version_2_with_negative else "squad")

    def compute_metrics(p: EvalPrediction):
        return metric.compute(predictions=p.predictions, references=p.label_ids)

    
    trainer = QuestionAnsweringTrainer(
        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,
        eval_examples=eval_examples if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        post_process_function=post_processing_function,
        compute_metrics=compute_metrics,
    )

    
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  

        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))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()

        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))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    
    if training_args.do_predict:
        logger.info("*** Predict ***")
        results = trainer.predict(predict_dataset, predict_examples)
        metrics = 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))

        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)

    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
    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

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


def _mp_fn(index):
    
    main()


if __name__ == "__main__":
    main()